Linda Burtch's blog

New Blog Site

noreply@blogger.com (Linda Burtch) — Tue, 23 Sep 2014 17:58:00 +0000

My blog has moved to its new home on our brand new Burtch Works site:

You can now find all of my blog entries - including the results to our latest communication skills flash survey - as well as our quantitative career resources, Burtch Works info, and (of course) jobs, all in one convenient place.

Check out the new site and let me know what you think!

CAP: The Certification Program for Analytics Professionals

noreply@blogger.com (Linda Burtch) — Tue, 19 Aug 2014 13:51:00 +0000

Scott Nestler, an experienced operations research analyst transitioning from the Army to the private sector, Chair of the Analytics Certification Board at INFORMS, and friend to Burtch Works, shares some thoughts on the Certified Analytics Professional (CAP) program.

CAP, Schmap. What’s That?

The Certified Analytics Professional (CAP) program has been in existence since the spring of 2013, after a couple of years in development. Since launch, participation has steadily grown; over 10% of the Fortune 100 companies now have at least one CAP on their staff. Here is some information about the program that might be of interest to both individuals seeking to distinguish themselves from the crowd and employers searching for qualified analytics talent.

It’s Not Just an Exam (There are Four Other E’s)

Many of the questions we receive are about the Exam that is part of the CAP certification program. While exams are understandably a source of concern to many, there are other components to the program. Collectively, we refer to these as “The 5 E’s.” The others include 3-7 years of Experience in the analytics field, with respect to Education, at least a Bachelor’s degree. Additionally, there is a requirement for the verification of Effectiveness of soft skills by a current or former employer or client. Finally, certificants must agree to a Code of Ethics, as described in this CIO Magazine article. Therefore, CAP is a well-rounded program of continuous professional development, not just a one-time event.

Now, about that practice-based Exam … you get 3 hours to complete 100 multiple-choice questions, based on typical tasks performed and knowledge applied by analytics professionals. The Job Task Analysis identifies 7 domains as shown in the table below; weights represent the number of questions in each area. It is available as a computer-based test at over 700 locations through Kryterion. Pencil-and-paper exams are available in conjunction with INFORMS conferences. Oh, and you get to use a 4-function calculator that we provide.

A Brief Primer on INFORMS

INFORMS stands for the Institute for Operations Research and Management Science; a professional organization with over 11,000 highly educated (50% PhD, 95% earned or pursuing MS) members. About half are academics while the remainder is split between practitioners and students. While based in the United States, about 20% of the membership is located in Europe or Asia.

Although INFORMS has been viewed by some as being traditional and academic in nature, that has changed since 2010, when it “caught the Analytics bug.” The CAP certification program is just one of several analytics-related initiatives at INFORMS, including a newly-developed Analytics Maturity Model, Analytics Magazine, an annual Business Analytics Conference, and Analytics Continuing Education Courses. Also, the semi-autonomous Analytics Certification Board includes members from across industry, academia, and government, including some well-known personalities in the analytics field, e.g. Tom Davenport (IIA), Bill Franks (Teradata), Jeanne Harris (Accenture), Kathy Kilmer (Disney), and Jack Levis (UPS).

More Information, Please

Those interested in more information about CAP certification can check out the CAP Candidate Brochure or Candidate Handbook, while employers might be interested in looking at the Employer Guide to CAP. If you have additional questions, please contact me, Scott Nestler, (acb@informs.org) or Louise Wehrle, the INFORMS Certification Manager (certification@informs.org).

Data Literacy in the C-Suite is Not a Fad, Neither is Data

noreply@blogger.com (Linda Burtch) — Mon, 11 Aug 2014 02:17:00 +0000

The conversation around Big Data has mostly shifted from “what is it?” to “how do we handle it?” and with this shift there has been much excitement around data scientists. But while data scientists are adept at many things, a large enterprise hoping to truly capitalize on the value in their data needs more than a team of brilliant data scientists – it needs a strategic leader capable of governing and managing the data, with the authority to enact strategy across departments.

At some organizations this has involved appointing a Chief Data Officer, and many more have appointed a senior leadership position with the same focus – but without elevating the role to the C-suite. Although the individual may not be called a CDO, it is more about the scope of responsibility than the title itself. Someone in the organization has to be ultimately responsible for the data.

Although many have been quick to brush this latest addition to the C-suite as just another fad, David Linthicum addresses this skepticism aptly when he writes:

“I’m not a big fan of creating positions around trends in technology. Back in the day, we had the chief object officer, chief PC officer, chief Web officers, you name it. However, data is not a trend. It’s systemic to what a business is, and thus the focus on managing it better, and centrally, is a positive step.”

Data is not a fad. In fact, data is exponentially increasing every day, hour, and second of the day, for every business. This means many things: increasing data management challenges, increasing opportunities to better understand customers, increasing privacy concerns, increasing advantages for marketing, and much more. Of the many uncertainties surrounding Big Data, its existence now (I’m referring to the data itself, not the buzzword) and going forward should not be one of them. When the conversation surrounding Big Data dies down, it will most likely be because massive data has become the new normal, not because it has disappeared.

CDOIQ

I was invited by Peter Anlyan to speak as a panelist at MIT’s Chief Data Officer and Information Quality Symposium (CDOIQ) in July, discussing how the industry is bridging the talent gap in analytics and data science. As we in the industry are all well aware, there is more focus than ever before on quantitative professionals, but the shortage of qualified analytics professionals and data scientists has made hiring a significant challenge for many companies.

The talent shortage is great enough, in fact, that some company representatives at the symposium expressed concern about sending their teams to Master’s programs for deeper training, lest they be poached away, defeating the investment of time and resources. While high attrition may be a frustrating symptom of the times, I’m not sure they have a choice.

Luckily, the increase in MOOC’s (Massive Online Open Courses) and various bootcamps across the country could offer an alternative to companies not willing to risk investing in a time and money into a full-fledged Master’s program. The efficacy of those methods however, depends on the strength of the program as well as the learning style of the individual, as Irmak Sirer of Datascope Analytics noted in his guest post last week.

Having just read Karen O’Leonard’s report from Deloitte, Show Me the Money: How to Secure Funding for Your Talent Analytics Case I was also eager to hear her thoughts on HR and talent analytics at CDOIQ, as well as attend some of the other events to hear more about the development of the Chief Data Officer position. You can read more about Karen’s thoughts from CDOIQ here, and Gregory Piatetsky of kdnuggets also had some good insights from the symposium.

The Future of the C-Suite

My thoughts on the longevity of the CDO role are that the responsibilities are the important part, not the title. Gartner predicts that by 2015, 25% of large global organizations will have appointed Chief Data Officers, so it will be interesting to see if that holds true. If we’re predicting the future in C-Level hires though, perhaps it’s time for a Chief Analytics Officer to throw their hat in the ring?

Becoming a Data Scientist: Master’s Program, Bootcamp, or MOOCs?

noreply@blogger.com (Linda Burtch) — Tue, 05 Aug 2014 01:07:00 +0000

This post is contributed by Irmak Sirer, a partner and data scientist at Datascope, where he solves business problems with data by designing analyses and interfaces. Irmak has helped companies across industries solve problems with data, from small companies to members of the Fortune 50. Working with Metis, Irmak is an instructor for their Data Science Bootcamp program which will be starting on September 2nd in New York City. You can read the full version of this post on the Datascope website.

One of the most frequent questions we hear, right behind “so, what exactly is a data scientist” or “what makes a great data scientist”, is “how do I become one? I should probably just get a Master’s, right?” Perhaps not anymore; rising costs, changing demand, and the Internet are disrupting this traditional path and providing two viable alternatives. At one extreme, self-learning through Massive Open Online Courses (MOOCs) give access to courses at an extremely low cost (often free), but leave it “as an exercise for the reader” to identify a suitable set of courses and tools to round out a coherent skillset. Bootcamps offer a middle ground where students can pay for a structured learning environment at a far more affordable rate compared with obtaining a Master’s Degree. So, “which path do I take?”

We think the answer to that question largely depends on the student. In some cases a student will prefer attending a bootcamp whereas in other cases a student will prefer receiving a Master’s at a university or taking university courses online through MOOCs.

Here at Datascope we see great benefits from the bootcamp format, so when Metis (a part of Kaplan) contacted us about partnering to design a data science bootcamp, we jumped at the opportunity. We thought we could take all these points we see as the advantages of the format, and elevate them as much as we could. So, we designed a course that would give aspiring data scientists a lot of experience with 4-5 projects, and a guided route of several core data science concepts and approaches. Participants can quickly build the necessary foundation without the burden of teaching herself everything or paying the handsome price of a Master’s program before realizing her dream job. If you’re interested, our Data Science Bootcamp program is starting on September 2 in New York (applications due by August 11), and you can learn more about it here.

Since there are many things to consider when choosing which program works best for you, in a separate post, we do a thought experiment to compare the three experiences for a fictitious aspiring data scientist named Audrey. For the sake of brevity, the following table summarizes our thinking about what each of these experiences is like and, more importantly, who they are ideally suited for.

	Masters	Self-taught (MOOCs)	Bootcamp
Learning	Theory-rich learning	Self-guided learning	Experiential learning
Teachers	Live university faculty professors	Recorded university faculty professors	Practicing data scientists
Outcome	Diploma	Certificate	Portfolio of projects
Duration	9 - 20 months	6 - 18 months (part-time)	2 - 3 months
Tuition	$20,000 - $70,000	$0 - $500	$0 - $14,000
Networking	1.5 years of social networking	Isolated; no in-person networking	Collaborative networking
Projects	Internship + practicum projects	Projects on own time	Projects built in to experience
Job hunt	University-wide recruiting day	Self-driven job search	Hiring day organized by bootcamp; talent placement manager helps with hunt
Ideal for	People that enjoy immersing themselves in campus life and want to take time to let the new material absorb while learning in a structured environment with the full credentials of a University degree.	People that thrive with ambiguity and self-guided environments and are motivated enough to design their own curriculum around their own strengths and weaknesses.	People that want to switch or accelerate careers ASAP and want to have confidence that the switch will result in a job they will like while learning in a structured environment.

As technology increases the rate of change of society, the most successful workers will be those that can quickly shift to new specialties and learn on the job to meet market demands. In our opinion, the bootcamp format provides the benefits of personalization, credentialing, and social learning that a Master’s degree offers, but at an accelerated rate with experiential learning. Sure it is more expensive than being self-taught, but the connection with employers and the guided, experiential learning process increases your confidence to tackle the uncertain prospect of making a career switch.

To become a data scientist, you don’t need to have postgraduate degrees, or 20 years experience, or be proficient with every data-related technique and tool under the sun. What you need is to have enough baseline knowledge and experience, and the skill to constantly adapt and learn. Bootcamps, in our opinion, are the perfect medium for making the transition.

Burtch Works' Best Advice on the Interview Process, Consulting Jobs, MBA's, and More

noreply@blogger.com (Linda Burtch) — Tue, 08 Jul 2014 20:04:00 +0000

In the thousands of conversations that Burtch Works’ team of specialized recruiters have had with analytics and marketing research professionals, we’ve answered many questions about the recruiting process, career paths, and the hiring market. For this blog post, I asked our recruiters about the most common questions they answer on a daily basis, and how they advise their candidates.

The Interview Process

Q: How long does the whole interview process normally take?

BW: Once a company has expressed interest in your resume, the interview process will involve one or two phone interviews, an in-person meeting (or two), and any follow up activity (i.e. background, reference, or credit checks), so 4-8 weeks is pretty standard.

There are, of course, some extreme cases: if a company has an urgent need they may make an offer very quickly, or on the other end of the spectrum, a very bureaucratic organization may take two months or even longer to make a hiring decision. Especially around vacation/holiday times, coordinating around multiple travel schedules can be an additional challenge when trying to arrange interviews.

Bottom line: Expect the process to take a little over a month, but it’s important to be flexible and patient. Check out our series of whitepapers which has great tips for professionals at every step of the interview process.

Consulting Jobs

Q: I hear consulting jobs pay well, what skills would I need to be a consultant?

BW: Consulting positions do tend to pay well in marketing research and analytics (to see how salaries break down in every industry check out our Burtch Works studies). The industry is seeing a lot of growth right now, and for the professional with great client-facing skills who doesn’t mind a rigorous travel schedule, there are a lot of great opportunities out there. I wrote a blog piece not too long ago discussing some of the other traits I typically see in successful consultants.

Bottom line: The consulting industry is growing and pays well, but make sure your communication skills are up to snuff and that your lifestyle is well-suited to a heavy travel schedule.

Pursuing an MBA

Q: Do I need to go back to school and pursue an MBA if I already have a quantitative degree?

BW: Although it never hurts to further your education, we generally don’t see employers specifically looking for candidates with a business degree in addition to a quantitative Master’s degree (or MMR for marketing research professionals). For certain leadership positions, employers may favor a candidate who can demonstrate their business knowledge and develop a strategic approach over a candidate who does not, but you may be able to develop those skills in leadership roles at work, not just from a business degree.

Bottom line: Developing business acumen might not necessarily mean pursuing an additional degree, if you can develop an understanding of important business concepts on your own.

Marketing Research Hiring Market

Q: What about for us marketing research folk, how’s the market looking?

BW: Our marketing research team wrote a great piece for American Marketing Association Magazine that offers an in-depth view of changes in the hiring market for marketing researchers. I highly recommend it if you want to stay aware of shifts in the landscape that may affect your career.

Bottom line: Lots of client-side opportunities are available, with more supplier side openings in the past six months. Most roles are Manager to Senior Manager level, with increasingly more Director-level and above.

Interested in more tips? Check out our new slideshares with career tips, hiring advice, and salary info, and download our studies or view all of our webinar recordings for free on our website.

Want the latest career news and research delivered straight to your news feed? Then be sure to follow us on LinkedIn, or follow @BurtchWorks on Twitter!

Burtch Works' Top Career Tips for Analytics & Marketing Research Professionals from Linda Burtch

Tell Your Kids to be Data Scientists – Not Doctors

noreply@blogger.com (Linda Burtch) — Tue, 10 Jun 2014 15:24:00 +0000

Recently I had the pleasure of being interviewed by John Phillips at CNBC about our data scientist salary study. His article, Why Your Kids Will Want to be Data Scientists, was published at the end of May, and in it he raised a very interesting point:

“According to Burtch Works’ 2014 study of salaries for data scientists… those responsible for a team of 1-3 earn [a median salary of] $140,000 and those responsible for a team of 10 or more earn $232,500.

By contrast, the mean average annual income for a lawyer in America was $131,990 in 2013, while doctors earned $183,940, according to data from the U.S. Bureau of Labor Statistics.”

Did you hear that? Data scientists earning more than doctors! For complete salary information for data scientists, Big Data professionals and market research professionals, download the full reports for free here. Salary is not the only reason however, that I would recommend encouraging your children to pursue statistics and coding over going to medical school.

How Data Scientists Are Supplementing Doctors

There are big changes happening in healthcare right now, and the implementation of EHR (electronic health records) in particular is a great example of how data scientists will be working with doctors in the future. The move to EHR is picking up steam, and the Center for Disease Control reports that 78% of office-based doctors are using EHR as of 2013, with that number only expected to grow as practices will face penalties for non-compliance. All of these electronic patient records spell out Big Data for the healthcare fields, and data scientists - like all quantitative folks - love data. These medical data could not only offer tremendous insights that change the face of modern medicine, but also offer rewarding opportunities to the data scientists who must decipher the data.

Patient care also stands to receive enormous benefits from data science. Venture capitalist Vinod Khosla was recently quoted at the Standford University School of Medicine’s Big Data in Biomedicine conference saying, “Humans are not good when 500 variables affect a disease. We are guided too much by opinions, not by statistical science.” While a doctor may be trained to look for many factors when diagnosing an ailment, some of these diseases are impossibly complex, and patients could stand to gain faster, safer treatment if left in the hands of a well-developed machine, or even a physician aided by one. For example, IBM’s Watson is already collaborating with Memorial Sloan Kettering Cancer Center to help doctors make better cancer treatment choices. The human interaction between patient and physician will continue to be important, but data scientists will have a measurable impact on the future of healthcare.

Career of the Future

One of my predictions for the analytics hiring market this year was that data scientists would be embedded in analytics groups, and with the internet of things, the increase in wearables, social media sentiment analysis and many more applications for data science, it’s no wonder this career has so much buzz around it. With the increase in demand, shortage of talent, high salaries and applications in every industry, data science is becoming a good option for career success. The road to that success begins with a strong early foundation in math, and (perhaps) some nudging from the parents. I can’t tell you how excited I was when my daughter, Becky (who started college this year at Macalester), changed her major to math, as I believe strongly that this is a career path that offers a bright future. Who knows, maybe one day I’ll have a data scientist in the family!

State of the Marketing Research Hiring Market 2014

noreply@blogger.com (Linda Burtch) — Tue, 03 Jun 2014 15:31:00 +0000

As part of my guest blogger series, the Burtch Works marketing research team, Karla Ahern and Naomi Keller, will be sharing some of their articles previously published in AMA magazine. In April Karla and Naomi published the following article on the state of the market, who's hiring, and which skills researchers need to stay competitive. You can also check out a video on the AMA site with highlights from the article.

State of the Market 2014

Last summer, we looked closely at trends in market research hiring as the economy moved towards recovery. At that time, we were beginning to see steady growth in job availability as well as a renewed sense of urgency within the marketplace. As more jobs became available, candidates were going on and off the market more quickly, often interviewing and fielding offers from multiple companies. As a result, hiring authorities recognized the need to act fast to snag the best employees and critically evaluate their compensation packages. Most of this activity was happening on the client side in the pharmaceutical, technology and retail industries. In 2014, we’re seeing this growth and urgency continue with some interesting new trends.

First off, we’d be remiss not to address the few rough patches in the landscape. As the economy continues to recover, retail and restaurants have been slow to return. The difficult winter has not helped these industries and some well-known companies have begun taking a hard look at headcount. Additionally, some CPG companies are seeing declines in market share, potentially due to consumer adoption of high quality, lower priced private label brands. However, in the field of market research, consumer behavioral changes inherently represent great opportunities for growth, and investment in research will always illuminate the best strategies to address these changes.

In our conversations with clients this year, a frequent talking point has been their recommitment to the expansion of consumer insights departments. This is great news for the industry as a whole, whether you work on the client or supplier side. Reinvestment in research should ripple throughout the industry and if corporate entities lead the charge, we’ll likely see suppliers and consultants staff up to meet the increased demand. So far, we’re seeing a split as departments expand; some are pulling their research more in-house while others are broadening their research capacity while continuing to outsource to vendors.

As a result of this growth, we’re also starting to see more Director-level roles open up. Last year the majority of openings were at the Manager to Senior Manager-level, but as companies commit to expanding their insights teams, they want experienced market researchers at the helm to guide long-term strategy and growth, and in some cases, help build a consumer insights function from scratch. This trend should prove beneficial for both junior and senior level candidates because as employees move up or out, roles will be back filled, allowing career mobility for junior and mid-level employees.

The expansion of consumer insights teams and the increase in Director level roles appears to also be contributing to an increased need for candidates with direct management experience. From manager roles upward, many of our clients are telling us that they want employees with demonstrated leadership success. Job seekers should be sure to highlight these skills on their resume and during interviews in order to stay competitive. For candidates without direct reports, we recommend emphasizing vendor management and project management experience if applicable.

After the long winter, the signs of continued job growth are clear and confidence is returning to companies and candidates. For those who were waiting out the economic slowdown, now may be a good time to start exploring the market. Even those who aren’t looking for a career change can benefit from understanding how the landscape is changing and how these new trends will affect their current role and their career overall. And as always, a conversation with your specialized recruiter is a great place to start.

Job Market Explodes For Quantitative Students

noreply@blogger.com (Linda Burtch) — Mon, 19 May 2014 14:34:00 +0000

With the market for quantitative candidates continuing to gain momentum, Burtch Works spoke with Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University - and friend of Burtch Works - about the job market for quantitative students, the beginnings of her MS in Applied Statistics program, and her thoughts on SAS vs. R.

Burtch Works: Tell me about the makeup of your class, do you find that many of your students are coming straight from undergrad or mid-career and retraining?

Jennifer Priestley: I would say 40% of our students come in direct from undergrad; the other 60% are coming in with work experience, and are doing a mid-career change. What’s fascinating to me is that we’re getting increasing numbers of MBA’s. For a long time, an MBA would suffice, and it almost didn’t matter where it came from, but those days are over. Maybe from the top five programs they still matter, but for the most part an MBA is a way to round out qualifications. It would be naïve to think that an undergrad degree in business and an MBA will get you somewhere.

BW: I’ve heard similar things from other programs about the influx of MBA students. Do you get many international students in your program?

JP: Some, but not as many as I’ve heard in other programs. Many of the students are first generation college students, so upon taking their first course here they will be the most educated in their family. Our students are motivated, driven, and hungry for success. We have a 0% unemployment rate, and when these undergraduate students are placed into white collar, professional jobs making $50,000 a year, they’ll be making more money than their parents. That’s what inspires me to do this, because you’ve fundamentally changed their path and lineage. They may start their education at X County Comprehensive High School, where only 3% of students even go to college at all, and by the time they graduate our program they’re working as a business analyst at Equifax. That’s a huge step.

BW: Can you elaborate on how the Master’s program came about? When did it start, and how did you choose what to include in the program?

JP: It launched in 2006. When we started, we looked hard at what other universities in the regional footprint were doing. We didn’t want to go head to head with Georgia Tech, University of Georgia, or Emory. We wanted to do something in statistics that didn’t compete with them, since we didn’t want to be competitive - we wanted to be complementary.’ So we started with a blank sheet of paper, and created our program to fill the gap. We wanted to build the program so that our students could graduate, and the next day walk into a Fortune 500 company and add value. The entire curriculum was SAS based, and programming in base SAS. We also stress the importance of learning how to extract, transport, load (ETL) and cleanse your own data. Statisticians don’t have the luxury of being a “data diva”.

Statisticians can’t just create models; they have to do computer science. The days of getting pristine data sets are long gone, so students need to know how to do these things. Basically, we were teaching data science before it was cool. In our program, students spend weeks in some classes just cleaning data before they learn the modeling techniques. We recognized a gap in the current education system, and combined the idea of ETL and cleaning with the mathematics and the statistics.

BW: What are some of the other challenges that your program aims to prepare students for?

JP: A big point in our class is communication skills. Nobody cares what you did if it is so complex that you can’t translate it. If you can’t explain what you did to a marketing major, then you can’t improve the decision making process, and you may need to consider hanging it up. The people who are going to take your stuff and do something with it are on the business side. As you move up the chain, you will have 5 minutes to explain what you’ve found to a c-suite executive who doesn’t care about your incredible skills, they just care about how it will profit the organization and affect the bottom line. You need to be able to convert the results into something meaningful, and if you can’t then it will reflect on you as a professional.

BW: How has the career outlook for your students changed over the past 3-5 years?

Exploded. Completely exploded. Like I said, we have a 0% unemployment rate. Companies will contact me, saying “Can you send us resumes for your students? We’re trying to hire for internships.” The press says there are no internships out there, but we actually don’t have enough students to place. When we launched in 2006, and basically we didn’t know, ‘if we built it would they come’. But, we graduated our first cohort in 2008, and they did well. I would say starting salaries in the $60,000-70,000 range, which is not bad. Now, we place kids in the $90,000 to six-figure range, so an amazing increase in salaries.

Another indication is the number of applicants that come to us. When we started the program we kind of said whomever wants to apply, we’ll let them all in. Now? We have so many applicants that we receive four applications for every slot. We also took the GRE requirements way up. Up until last year they had to be above the median for us to look at the application, and now they must be in the 75^th percentile for us to consider the application. Keep in mind, as I said, we don’t have that many international students.

BW: So I’ve been hearing a lot about SAS Day at KSU, and it seems like a fascinating concept. When did that start, and how has it evolved over the past few years?

JP: It started in 2007. We work very closely with SAS Institute, so the year after we launched the program I got a call from a sales representative for SAS. He does business development for Fortune 500’s like Delta Airlines, Equifax, all companies in the region, and he said to me, “I’m always challenged with, once they get a multi-million dollar infrastructure with SAS, the next question is always: How do I find people to run it?” It’s easy to find someone with 5+ years of SAS experience for $200,000. What’s harder is finding someone with 2-3 years experience for $50,000-100,000. He said, “You have a natural pool of talent, and we have a built-in demand from companies.”

It eventually evolved into what we have now, where the first half of the event is the student poster competition, with students creating a poster for a SAS-based project, and the alumni come back and judge. We probably had 70 posters last year. We look for the perfect intersection of correct math and an elegant, efficient, and visually appealing presentation. Students set up a booth, and companies can visit the students and drop off their business cards. It’s basically a reverse career fair.

BW: What kind of companies go to SAS Day, and how many of them?

JP: Of the 200 or so people who came last year, I’d say there were about 50-70 different companies represented. Companies like Autotrader, Deloitte, Blue Cross Blue Shield, Equifax, KPMG, Coca-Cola, The Southern Company, Maxum Insurance, Teradata, CarMax, State Farm, Aspen Consulting, Midtown Consulting Group, Slalom Consulting, and AT&T.

BW: Do you have any examples of other schools that do this? Is this a common thing?

JP: SAS had said they wanted to partner with other schools, and I’ve heard of similar events at Texas A&M, UConn, and University of Illinois. I know University of Alabama has something similar, like a Data Analytics Week that’s aligned with the business school.

BW: What do you think of the popular debate about SAS vs. R?

JP: It’s not even a debate. We don’t see SAS and R as competition. They’re complimentary, and a lot of companies use both. Although we partner with SAS, we still teach both programs. I’d say we’re 70-80% SAS dominant, but we teach R also, and we encourage our students to know both. That’s our philosophy. We’ve actually had an R Day also for 2 years now; the next one will be in the fall.

I would say the results of your survey, 65% SAS and 35% R, is a perfect example of what we use.

The Deep Dive: SAS vs. R

noreply@blogger.com (Linda Burtch) — Mon, 28 Apr 2014 20:20:00 +0000

Last month I conducted a quick “flash survey” of my network to quantify the preferences of the Burtch Works network, and asked: Which do you prefer to use, R or SAS? I posted the initial results on my blog a few weeks ago, and as promised during the webinar for our Data Scientist Salary Study, we've finished up our deeper dive analysis of the data from over 1,000 respondents. Whether you think the results are predictable or surprising I’d love to hear your thoughts in the comments below, as the conversation has been pretty lively so far. Without further ado, here are the greatly anticipated results!

As many theorized, respondents with five or less years of experience were the most likely to favor R.

Although SAS was favored by most education levels, amongst PhD respondents R was almost even with SAS. In looking at PhD respondents with five or less years of experience, R was decidedly more popular than SAS.

In most regions of the United States SAS was the preferred tool, however on the West Coast R is favored over SAS.

In almost every industry SAS is preferred over R, except for Tech, Telecom and Gaming companies. Retail, Pharma/Healthcare, and Financial Services have the strongest preference for SAS.

The Great Debate: SAS vs. R

noreply@blogger.com (Linda Burtch) — Fri, 04 Apr 2014 18:05:00 +0000

I’ve been recruiting analytics talent for over 30 years, and now over the past few years I have watched open source R seemingly catapult to popularity alongside the proprietary standby SAS. Despite hearing more about R from clients and candidates than ever before, determining whether R was actually more popular than SAS proved difficult. A quick Google search for “R vs. SAS” returns more than a few pages dedicated to each side, as well as several heated LinkedIn discussions relating to the topic, with no definitive answers.

For my latest “flash survey” I wanted to quantify the preferences of the Burtch Works network, and asked one simple question: Which do you prefer to use, R or SAS?

With even more participants responding that couldn’t seem to pick just one or picked neither, this tells me that such a seemingly simple question has a more complex answer. Here are just a few of the entertaining responses we received:

“I am a purist, so SAS.”
“R - unless you have a ‘both rock’ category – it’s a close one.”
“Never learned how to use R. Too damn old.”
“R. But isn’t the debate more between R and Python?”
“SAS as long as I’m not paying for it.”
“SAS. What’s ‘R’? (Joking…)”

Curious as to how these results may vary by factors like industry or years of experience? I know I am! I can’t wait to dig into the data, and in the next few weeks will be posting a full write-up on the blog with our findings. Thanks to all who participated and stay tuned!

Edit: To see our deeper dive into the survey results, including how preferences vary by years of experience, education, region, and industry, please visit The Deep Dive: SAS vs. R

To be the among the first to see all our latest job postings, blog posts and news - including our upcoming Data Scientist Salary Study - be sure to follow Burtch Works on LinkedIn.

Tips for Hiring Data Scientists

noreply@blogger.com (Linda Burtch) — Wed, 12 Mar 2014 14:50:00 +0000

This post is contributed by Frank Lo, an experienced data science professional and friend of Burtch Works. Frank is currently the Head of Data Science at Wayfair, as well as the founder of DataJobs.com.

There is so much hoopla around the need to hire data scientists -- but amid all the frenzy, I notice a major disjoint between what companies think they need and what they actually need to leverage data science for business value. I come from the background of leading a data science team; on top of cultivating the team and diving into the nitty-gritty of data, I spend a lot of time recruiting, trying to find new rock-star talent. I'd like to share a few things I've learned along the way around what to look for.

Look for quant experts with business hustle.

First, let's think about what is data science. It is not only a combination of technical and quantitative disciplines, but also the acumen to leverage STEM skills to transform business. Too often, we focus too much on pure engineering/math ability, kicking business smarts to the wayside. On the contrary, I think business acumen is one of the most important traits of effective data scientists -- so much so that I filter out strong tech/math candidates if they have trouble thinking through the business applications of their quantitative work. Ultimately data scientists create value by being consultants to the business. Data mining and predictive analytics by themselves are not the point, but rather the means to enable intelligent strategy development. Look for people who are good at all of the above, including business.

The #1 intangible is intellectual curiosity.

The spirit of data science is discovery. Given a mountain of data, what inferences can we make? What truth is revealed or predicted? The strongest data scientists are motivated by this curiosity to explore data in very creative ways. When I recruit for my own team, I look for people who are not only good at answering questions, but who want to ask their own questions. This genuine inquisitiveness is rocket fuel in driving a data scientist's search for meaningful discoveries in data. It is so critical to the role that we turn away candidates who cannot demonstrate that they are brimming with intellectual curiosity.

How to screen for this intangible? One interview question I ask everyone: "tell me about a data science project or investigation that you initiated on your own, outside of school and work." I look for candidates who can respond to this at length, diving into quant, tech, or business problems they were so intrigued by that they carved out their own time to pursue. It is a great signal they will thrive in a data science role.

Don't filter candidates based on degree.

There is a notion out there that the best data scientists are the ones with Ph.D's. From my experience interviewing and evaluating candidates, it is my opinion that academic degree is the last consideration that matters. Some Ph.D candidates have very well-rounded skills and do become top performers. Though, I've found that many others find themselves mentally stuck far down the academic rabbit hole, and have difficulty translating their focused depth into value in a business environment. My point is: degree by itself is a very incomplete indicator, so don't filter candidates on it. Consider all academic backgrounds, zeroing in on well-rounded skills paired? with the right intangibles (i.e. intellectual curiosity, business acuity, etc). You'll find that many star data scientists are self-taught hackers and mathematicians, who may not have wanted to sacrifice work experience for academic credentials, but have a very complete data science knowledge base.

Clearly, there is a lot to consider in how to hire solid data science talent. The reality is that data science is very multidisciplinary; you're looking for a blend of skills that can reveal itself across a wide range of academic backgrounds and professional experiences. But as long as you have a solid understanding for the nuance in what makes a good data scientist, you'll have an easier time trying to identify the right people for your team.

Keep an eye out for our Data Scientist Salary Study - a follow up to the landmark Burtch Works Study: Salaries of Big Data Professionals - which should be released in the next month.

Flash Survey Results Part 2 – How Do Quants Find Their Jobs?

noreply@blogger.com (Linda Burtch) — Mon, 03 Mar 2014 19:12:00 +0000

In my last blog, I posted results from a short “flash survey” of my analytics connections about their motivating factors when changing jobs. Although there are articles about where candidates in general find their jobs, I wanted to see how different those numbers might look for analytics professionals in particular. The second question in my quick survey was:

2.) What was the source of your last job?

Key Insights

Not surprisingly, the most likely source for placement is someone within your network, with 36% of respondents having found their position through a referral or networking.
The second most likely source is to apply directly with 30% of participants.
Executive recruiters and corporate recruiters account for roughly one-third – 34% combined – of all job sources.

It was not surprising to me that referrals and networking are the most common way that analytics professionals find their jobs. Recruiters show good representation in analytics (34%) when compared to the results from the general candidate population (16%). Working with a specialized recruiter with whom you have developed a strong relationship can be like an extension of your network, and a very well-connected one at that. Especially in such a specialized field, it works to your advantage if your recruiter can speak the same language that you do. It will be interesting to see how these numbers will change in the future.

Flash Survey Results Part 1 – What Motivates Quants to Change Jobs?

noreply@blogger.com (Linda Burtch) — Mon, 24 Feb 2014 19:58:00 +0000

We’re midway through the first quarter and the analytics hiring market is heating up – organizations are evaluating their business goals and hiring needs, and candidates are evaluating their career options for the coming year. As a follow up to my blog post with my predictions for the analytics hiring market, I decided to conduct another “flash survey” of my connections to see what motivates them to make a career move and how they found their last job. I’ve read studies about how Quants are most motivated by intellectual curiosity, and was interested to see if this would translate to their career aspirations. I received 216 responses to my quick survey, the first question being:

1.) What would be your top two motivating factors if you were to change jobs?

Key Insights

Overall, salary and opportunity for growth were the top two motivators for respondents, with 47% and 44% respectively.
Challenging work was third with 38% of respondents choosing this in their top two motivators.
A change in company (16%) and industry (11%) were the factors least likely to inspire a job change.

Conclusions

Quantitative candidates are more in demand than ever, and if the results of this poll are any indication the talent pool is ambitious and hard-working. Their focus on salary is interesting, and reflects what I found in the Burtch Works Study last year – that Quants changing jobs can expect a 14% pay increase. When compared to the average 2-4% yearly increase they can expect if they stay at their jobs, it’s no wonder the hiring market is humming with activity and that salary is a top motivator.

Opportunity for growth was a close second, and it seems that analytics professionals are evaluating “promotability” when considering new roles, compared to the opportunities available in their current organization. For my analytics job seekers, I wanted to offer a word of warning here – although you can realize an increase in pay, I usually don’t see candidates substantially improve their title when changing jobs. It’s much more common to see a candidate make a lateral title move when changing companies, knowing that there is a greater possibility for a promotion down the line in their new organization because it is in a growing field or expanding.

Stay tuned for my next blog when I will post the results from my survey’s second question, “What was the source of your last job?”

My Predictions for the 2014 Analytics Hiring Market

noreply@blogger.com (Linda Burtch) — Wed, 22 Jan 2014 17:18:00 +0000

Thanks in part to the media coverage of Big Data, analytics has become inescapable. To those who have worked in and around this industry for years this information is nothing new - merely more visible. Every day I’m hearing about more and more businesses warming to the idea that there are actionable insights readily available in their data if they know how to use it.

It is an exciting time to be a statistician, data scientist, or analytics professional, and if you thought 2013 was big, 2014 promises to be even bigger. Without further ado, here are my top predictions for the analytics hiring market:

1.) Real-time analytics becomes increasingly important to business intelligence – Industries like the gaming industry already give their customers incentives in real-time and retailers are quickly moving toward real-time couponing. Those of you with real-time data experience will be in high-demand for these growing roles.

2.) Data scientists will be embedded in analytics groups – In addition to analysts who can build complex models, there will be an increasing need for data scientists, who with their background in analytics and computer science can wrangle massive, unstructured data sets. Companies hoping to extract the full benefit from their data sets will need a combination of both groups.

3.) If you don’t have digital you will be a dinosaur! - If your skill set doesn’t already include digital analytics your career plan should be to get experience and soon. Without it your skills will become outdated and obsolete.

4.) Sentiment analysis will continue to erode traditional survey methods – Use of social media data and other “free sources” has the opportunity to overtake traditional survey methods, which will impact the marketing research talent landscape.

5.) Corporations will bring on internal staff to replace high paid consultants- Consultants got the ball rolling, and now companies will seek to take on full time staff.

6.) However, this will not adversely affect the consulting industry – Demand is so high for analytics experts (and continuing to grow) that there will still be companies looking to small and large consulting firms. Despite some companies hiring full time staff, the consulting industry will continue to experience growth. Heavy travel schedules will continue to be the norm for these opportunities.

7.) The trend towards wearables and PEDs will create new positions- If the buzz from CES2014 is to be believed, the wearables trend is increasing. As with any new technology that creates massive amounts of data, analytics positions will be created in the industry to make sense of and capitalize on all that information.

8.) Startups’ grip on the hiring market will loosen- I have spoken with candidates who have worked with many failed startups, and I think that this year the tendency towards hopping from failed startup to failed startup will begin to shake out. Candidates who have been through the failed startup cycle will begin to realize that to build a career it will be important to learn from an established group.

9.) Graduates from the new analytics programs will begin to hit the job market - This is not so much a prediction as it is inevitable, and I am very interested to see how well these new programs have prepared their students to enter the quantitative workforce.

What trends do you think we will see in the analytics hiring market? Let me know what you think in the comments below.

Burtch Works' Most Popular Social Media Posts from 2013

noreply@blogger.com (Linda Burtch) — Thu, 19 Dec 2013 21:43:00 +0000

2013 was a busy year for Burtch Works’ social media accounts, and I wanted to take the time to revisit some of our most popular links this year.

Our top links include blog posts on topics from resume writing to data science wannabes, as well as links to our original research, including salary studies and a flash survey of our network.

Here is our social media year in review:

1.) The consulting trend is really gathering steam but the opportunity is not for everyone, which I addressed in Should You Take That Consulting Role? Here's Why or Why Not.

2.) With networking being more important than ever and holiday parties around the corner these 18 easy conversation starters from Careerealism that I posted were perfectly timed.

3.) Apparently everyone is thinking about retooling their resume, because my latest blog post Need to Rewrite Your Resume? Four Tips Before You Submit is one of the most popular posts on our social media this year.

4.) Are you a real data scientist? Or just a Data Wannabe?

5.) McKinsey put out an informative infographic on Big Data and ROI Big Data Big Profits.

6.) Lou Adler once again published a great article on LinkedIn with 5 Things You Must Not Do In an Interview, and 5 Things You Must.

7.) The Burtch Works Study: Salaries of Big Data Professionals was released in July, and Silicon Angle ran a story about lucrative salaries for foreign-born analysts.

8.) Our Marketing Research Salary Study was released in October, both salary studies and their webinar presentations are available here.

9.) Back in April I addressed the lack of urgency to hire in Help Wanted But Hiring Slow.

10.) While catching up on TEDxTalks in September I watched Big Data, Small World by Dr Kirk Borne which is definitely worth your time. He also maintains a very active twitter presence and was voted the #1 influencer on Big Data by Onalytica.

11.) In a guest post on my blog at the beginning of November, Burtch Works’ entry-level recruiting specialists shared their advice in How to Get Your First Analytics Job.

12.) Researchers at the University of Pennsylvania analyzed social media Big Data to create word clouds showing commonalities by age, gender & personality. Fascinating stuff!

13.) Forbes published my article Five Ways Marketers Can Keep Quants From Quitting in December.

14.) We tallied results from our Flash Survey for analytics professionals about how many are approached via LinkedIn about new job opportunities and how often.

15.) What Design Thinking Can Teach Analytics Professionals was an attention-grabbing article from Data Informed.

With all the attention focused on Big Data, I expect next year to be even busier as more companies look beyond the buzzword and start seeing returns on their investments. The analytics hiring market will continue to heat up, so keep an eye out for my 2014 hiring predictions blog in the beginning of January. For more career advice, blog posts and industry news throughout the year be sure to follow Burtch Works on LinkedIn and Twitter. Happy Holidays everyone!

Need to Rewrite Your Resume? Four Tips Before You Submit

noreply@blogger.com (Linda Burtch) — Mon, 09 Dec 2013 19:23:00 +0000

As you probably know, expecting to land an interview without revamping your resume is a costly mistake. Obviously your resume should reflect any changes in your work history, but it should also grab an employer's attention and convince them that you are worth interviewing. Since their first impression of you will likely be the resume you submit, you should make sure to put time and thought into crafting one that tells your story and emphasizes your unique skill set.

In continuation of my guest blogger series, Burtch Works' marketing research specialists Karla Ahern and Naomi Keller will be sharing their top tips for writing a resume. With years of experience viewing many (many) resumes for our candidates, there are four main areas that demand your attention. You can find the original article posted here.

As recruiters and former market researchers, we have a unique vantage point into the field of market research. We have both an intimate, experiential knowledge of the day-to-day life of market research, as well as an overall view that allows us to see the varying trends that affect the marketplace. As a result, our candidates come to us with a variety of questions regarding their search process and “How can I improve my résumé?” is one of the most frequent ones we hear. A strong résumé is compelling and concise, and it effortlessly tells your story. Here are our top tips to achieve that effect:

1. Highlight the impact you’ve had at your organization, not just your day-to-day responsibilities.
As a market researcher, you already understand the importance of telling a story. It’s not enough to provide data tables for a study; you have to provide insightful analysis and connect it to the overall effect for the brand. The same goes for your own résumé. While your responsibilities are a crucial element of your story, employers are always looking for that last impactful punch, so be sure to explain how you contributed to the success of the company in a quantifiable way.

2. Add summary and objective statements.
Don’t make the mistake of thinking that your work history will tell your story for you. Adding a summary and objective statement to the top of your résumé is your chance to contextualize your experience and craft your career narrative. You want to boil down the essence of your background and phrase it in a compelling way that draws a clear line between your achievements and your career objectives.

3. Include your core competencies.
Similar to summary statements, we recommend that candidates include a list of key competencies at the top of their résumés. This is your opportunity to call out your methodological expertise and specialized skills in a visually impactful way. As with any résumé, tailor the highlighted skills to the specific position that you’re seeking. If it’s a consumer insights manager role, for example, you’ll want to highlight the types of survey methodologies that you’re familiar with, quantitative and qualitative research expertise, vendor management experience, etc.

4. Keep your LinkedIn profile fresh.
Sometimes having a polished résumé only goes so far. There are hiring managers and recruiters cross-checking you on LinkedIn, not to mention sourcing for positions that you may want but don’t even know about. Keeping your profile up to date is imperative. It’s fine to be selective about what you share on LinkedIn, but at the very least, make sure your profile is current to your latest job, consistent with your résumé for all dates and titles, and tightly edited, and includes an overall description of each of your roles.

Ultimately, the most important thing to remember when crafting your résumé is that the first make-or-break initial scan will be brief, often less than 20 seconds. Your goal should be a concise, organized and memorable résumé that tells your story, both where you’ve been and where you intend to go.

Should You Take That Consulting Role? Here's Why or Why Not

noreply@blogger.com (Linda Burtch) — Mon, 25 Nov 2013 17:02:00 +0000

As many companies are keeping a tight rein on headcount costs, I’m seeing an uptick in available consulting positions. An increasing number of the marketing analytics positions we’ve been working on have been with consulting firms. Although outsourcing to control headcount is not a new trend, the increase means that there could be more opportunities available to someone who is open to the possibility of a consulting role.

In our recent Burtch Works Study: Marketing Research Professionals and Burtch Works Study: Big Data Professionals we discovered that consulting pays well for both groups, and I am interested to see how the current trend affects salary and demographics over the coming years.

For someone whose lifestyle can accommodate a heavy travel schedule there are certainly advantages to taking a consulting position; since your clients may be in a wide variety of industries it is a great way to gain exposure to different industries. It is also a great opportunity to build a network beyond your colleagues. As you gain exposure with high level corporate professionals keep in mind that these connections will benefit you throughout your career.

There are several things to consider however, before pursuing a consulting position. There will almost always be heavy travel involved, with a typical schedule of Monday through Thursday traveling and Friday working in the local office. So if you have a young family or your lifestyle cannot accommodate a rigorous travel schedule, then it might not be the best choice for you.

In addition, as you take on a more senior role you will be expected to drive business and revenue, and contribute to the growth of the business, at which point business development skills will be crucial for success.

For quantitative and marketing research professionals alike the increase in consulting positions could present a lot of opportunities, but it’s always important to consider and balance your lifestyle goals with your career goals before committing to anything.

Regardless of your career goals however, you should position yourself in a way where you are the asset – your knowledge, your skills, and your unique perspective. I always advise my candidates to make sure that you are a value-add to your organization; by doing so you ensure that you will always be marketable, whether your goal is a promotion or a job change.

Career Advice and Trends for Marketing Research Professionals

noreply@blogger.com (Linda Burtch) — Tue, 19 Nov 2013 20:24:00 +0000

As part of my guest blogger series, the Burtch Works marketing research team, Karla Ahern and Naomi Keller, will be sharing some of their articles previously published in AMA magazine. In the June 2013 issue Karla and Naomi published the article below about hiring trends they are seeing in the field, as well as career advice for marketing research professionals. They also recently released The Burtch Works Study: Salaries for Marketing Research Professionals, which looks at salary information and how it varies by geography, education, industry, career level and more. Prior to joining Burtch Works, Karla and Naomi worked in client services, account management and business development roles at research firms including GfK, Ipsos and IRI.

Career Advice for Researchers

When we think about the signs of healthy economy activity and recovery from the recession, it’s all too tempting to have tunnel vision in lieu of reports that claim that companies are still cautious on the hiring front. But when you look at businesses’ day-to-day hiring practices, there is no denying that a sense of urgency is returning to the market.

In the past months and years, our team that recruits for professionals in market research witnessed the frustrating trend of human resources and hiring managers failing to move quickly enough. It was not uncommon to interview for months only to wait even longer for a final decision and offer to be made. Companies wanted to make sure that the worker they hired was fully committed for fear that she was only making a move for shortsighted reasons or out of desperation after a layoff. But the tides appear to be turning. Hiring authorities are finally starting to realize that if they sit around waiting for an impossibly perfect candidate, or if they drag their feet during the interview process, candidates have no problem going somewhere else.

A Different Sort of Recovery

Rest assured that economic recovery is happening. It’s not the same kind of bounce-back that we may have seen in past recessions, but we’re witnessing a slow and sure positive change as companies look to increase headcount in market research. Most notably, we have seen increased needs in the pharmaceutical, technology and retail industries, mostly at the middle to senior level. Big-name firms never used to have a problem attracting top-grade talent to join their teams, but times are changing.

The good candidates are out there. If companies are having trouble hiring them, it’s because workers are being courted by multiple companies at once. As soon as a well-qualified candidate begins his job search, multiple employers may be interested. It’s interesting to note that many of these prospects are client-side opportunities as of late, suggesting that this may start to become a trend in job growth in the coming months and years.

Large CPG firms that used to be able to count on their names and reputations now are competing for candidates’ attention, as exceptional workers are weighing multiple offers and can afford to be picky with their choices. For clients, it’s important to know what they’re competing against: aggressive salaries, growing bonus potential, good career trajectory and relocation assistance, for example.

What Makes a Good Candidate

Of course, this is excellent news for market research candidates. We have noticed that those with four to eight years of experience in consumer insights are seeing a lot of activity, especially if they’ve had experience on the client or corporate side. Again, we’ve seen this sector becoming more active lately, so it’s a good indicator of what we can expect in the near future.

Candidates with an advanced degree, either a master’s degree, or an M.B.A., typically attract more attention from employers. Continuing education is always a good investment, but with today’s competitive market, giving yourself another advantage against other workers pays off. We often talk to clients looking to fill more senior positions who only want to consider candidates with the aforementioned degrees and this will only become more of a requirement for senior-level positions in the future.

Flexibility with location also is a desirable trait for candidates today. While major metropolitan areas have always been a hub for market research careers, large corporations have long had opportunities available in less-populated areas in the Midwest and Southern regions of the US. Candidates who are open to relocating are able to explore more options, and more companies are starting to offer competitive relocation assistance packages that we haven’t seen for years.

Flexibility on the title is another, sometimes more challenging, trait to find in candidates. Although workers should consider career growth and management experience in their job searches, it’s important to remember that a title isn’t everything. Your title can vary from company to company, as different employers use various internal classifications. A senior analyst at a small boutique firm means something quite different at a Fortune 500 company. Understand that when switching industries, lateral moves are sometimes necessary to gain experience in the long run.

The coming months should provide more insight into what we’ve already been seeing, namely the growth in shopping insights and client-side options. Market research was named one of the hottest jobs in 2012, and we have no doubt that 2013 and 2014 are sure to follow suit.

How to Get Your First Analytics Job

noreply@blogger.com (Linda Burtch) — Wed, 06 Nov 2013 16:22:00 +0000

Over the past few months Burtch Works’ entry-level recruiting specialists Erin Craig and Erinn Tobin have been visiting colleges and universities to meet with students who are preparing to enter the field of analytics. After securing a degree in statistics, mathematics or other related fields, the next challenge for many students is their job search. This will be the first job search for many of them and I wanted to give Erin and Erinn an opportunity to share some of their most helpful tips for students. Since they receive a lot of questions from students on their campus visits, I will also be inviting them to post their answers on my blog as guest contributors in the coming months.

Burtch Works’ Top Tips for Entry-Level Candidates

1. Utilize LinkedIn – Over 90% of corporate and independent recruiters who recruit using social media use LinkedIn. It is fast becoming the go-to resource for companies to check your references and resume, as well as a resource for job seekers to stay updated on company news, search for job postings and develop their network. Having an updated, professional profile on the site allows companies with whom you are applying or interviewing to see you as a person they might want to hire, not just another anonymous resume.

2. Complete an Internship – A great way to test your skills, continue learning and expand your network is to complete an internship. Without previous work experience to go on, prospective employers will look at internships (as well as coursework) to determine if you might be a good fit for their organization. Sometimes - if a company is looking to hire full time and you demonstrate an exceptional work ethic- an internship may also lead to a job offer.

3. Get Your Hands On Messy Data – One of the biggest challenges students will face in their first analytics job is the lack of experience they have with real-world data sets, so in addition to completing an internship your strategy to enhance your resume must include working with unstructured data. Two great online resources we would recommend are Coursera and Kaggle: Coursera is an MOOC (Massive Online Open Curriculum) where you can take free courses to further your education and Kaggle hosts data science competitions where you can not only test your abilities against other members, but also get access to large, unstructured data sets more similar to the ones you might use at an analytics job. Completing your SAS certification can also add credibility to your analytic skills and as many companies adopt other tools - such as R, Python, SQL, etc. – you will have a significant advantage if you diversify your skill set.

4. Leverage a Recruiter – Developing a relationship with a recruiter early in your career has many advantages: companies will often have open positions that they fill by working with recruiters (not by posting them on job boards), your resume will be seen by a hiring manager instead of disappearing into a pool of other resumes in their online tracking system, and it lends a more personal experience to what can be a very daunting hiring process.

Burtch Works sends out monthly emails to students that cover all the topics that, in our experience as quantitative recruiters, can help you prepare for the road ahead. Want to learn more about the interview process, how to get high-quality references and what you can expect at your first job? Don’t miss out! Contact Erin Craig ecraig@burtchworks.com to receive more expert advice from our recruiters and be on the invitation list for our career webinars. Check back soon, when Erin and Erinn will tackle some of your job-search questions.

Kaggle Flourishes by Embracing the Competitive Nature of the Data Scientist

noreply@blogger.com (Linda Burtch) — Thu, 12 Sep 2013 17:12:00 +0000

The emergence and success of Kaggle, a website dedicated to matching number crunchers with companies in need, shows us that no matter how challenging the problem there is always someone willing and eager to tackle it head on.

We’ve been hearing more and more about Kaggle in various capacities: candidates posting their Kaggle ranks on their LinkedIn profiles, students or recent grads using the site to gain experience with unstructured data, or even companies asking for candidates’ Kaggle rankings in job postings. For an aspiring data scientist with little to no experience this could be a invaluable tool to gain experience with real-world big, messy data.

Kaggle has grown in popularity from 25,000 to over 100,000 members in a little over a year and received attention from numerous publications such as The New York Times and Forbes about its unique service. Quantitative professionals from all over the globe can compete for prize money by offering intellectual property including any algorithms, models, solutions etc.

In addition to earning money for their services, Quants have the opportunity to hone their skills and see how their solutions and skills rank against other members. For companies who cannot afford or do not need a full time analytics staff, Kaggle will match you with a team based on your specific initiative that you can hire on a contract basis. According to the founders of Kaggle, their intention was to inspire great performance by inciting competition amongst the members.

The competitions are divided into sections, with one dedicated to “Getting Started” which offers entries and students looking to test their skills on real data sets an opportunity to see how they compare to working data scientists worldwide. There have also been competitions from Belkin, Amazon and Expedia for more advanced participants.

Most recently, Facebook is using Kaggle to host a recruiting competition. Could this site completely revolutionize the way Quants learn, share, compete or job-hunt within the community? As anyone keeping abreast of Big Data news will tell you, we’re moving into the age of digital everything- socializing, interviewing, learning- why not digital sports for the data scientists too?

Flash Survey Results for Analytics Professionals!

noreply@blogger.com (Linda Burtch) — Wed, 07 Aug 2013 18:32:00 +0000

As you probably already know, I’m always interested in gathering information from and about the analytic community. Our salary study for Big Data professionals is the biggest project we’ve taken on within this mission, but recently we conducted a very brief “flash survey” of our connections regarding a topic that I’m hearing about more and more: individuals’ experiences with being contacted by recruiters via LinkedIn. As a recruiter, I use the site quite a bit, and our staff has participated in a number of training sessions made available by LinkedIn. In these sessions, it’s frequently quoted that an overwhelming 95% of LinkedIn members say they are open to receiving messages from recruiters on LinkedIn.

In this brief survey, I asked our quantitative network three simple questions:

1. How often are you contacted about job opportunities through LinkedIn?

2. How frequently do you respond?

3. Are you actively considering a job change?

We received a great amount of feedback from this quick poll, and in the end we analyzed the responses of 481 analytics professionals. Using information from our database, we were able to categorize these responses further based on geographic region, and career level, using the same hierarchy of levels that we used in the Burtch Works Study. As a reminder, here is how we define job levels and regions:

Individual Contributors (IC) are professionals without people management responsibility.

Level 1: responsible for learning the job and being hands-on with analytics (typically 1-3 years’ experience)
Level 2: hands-on with data, working with more advanced problems/models (typically 4-8 years’ experience)
Level 3: considered an analytics Subject Matter Expert (typically 9+ years’ experience

Managers(MG) are professionals whose responsibilities include people management.

Level 1: tactical, leading a small group within a function (typically 1-3 reports)
Level 2: leads a function and executes strategy (typically 4-9 reports)
Level 3: member of senior management who determines strategy (typically 10+ reports)

We uncovered a couple interesting findings, so let’s take a look at what the data revealed.

#1: How often are you contacted about job opportunities through LinkedIn?

Key Insights

More than 25% of analytics professionals are contacted at least weekly, with an additional 63% reporting that they are reached out to at least monthly.
The most senior level candidates (MG, level 3) reported being contacted most frequently, with 60% saying they are reached out to at least weekly.
Overall, managers are reached out to more than individual contributors with an average of 42% of managers reached out to at least weekly, compared to only 21% for individual contributors. We have two hypotheses for why this may be:

o Many organizations look to put key leaders in place prior to hiring the core staff that will be doing a lot of the analytics. It may be that managers are being targeted now, and as those positions fill, the emphasis will trickle down to the individual contributors.

o Job titles for management are much more consistent (i.e. Manager, Director, VP) than those of individual contributors, who notoriously have a multitude of titles (e.g. Analyst, Specialist, Scientist, Statistician, Consultant). This means sourcing by titles is more challenging at the individual contributor level.

Not surprisingly, analytics people on the West coast are contacted most frequently, with 93% of west coasters approached at least monthly.
People in the Northeast and Midwest were approached in similar amounts – roughly 89% are contacted at least monthly.

#2: How frequently do you respond?

Key Insights

More than 50% of quantitative professionals report responding to recruiters’ message on LinkedIn almost all of the time or always.
Tactical 50% of quantitative professionals report responding to recruiters’ message on LinkedIn almost all of the time or always.
Individual contributors and managers, however, have very similar response rates overall.
West Coasters are the most likely to get back to their suitors on LinkedIn: 64% reply almost all of the time or always.
People living in the Mountain region are least likely to respond, with only 38% replying almost always and 53% replying half of the time.
Quants in the Northeast were fairly likely to respond (53% almost all of the time or always, 40% at least half the time), showing similar response rates as Midwesterners.

#3: Are you actively considering a job change?

Key Insights

69% of Quants are at least willing to entertain the idea of a job change, or are actively looking.
Even though 30% of respondents aren’t considering a change, many respond to messages anyway given that only 7% said they never answer these messages.

Conclusions

Over the years, LinkedIn has not only become a powerful networking tool, but also a key recruiting tool. Professionals from almost every industry have flocked to the site, set up a boiled down (or not) version of their resume as their profile, and used it to connect with colleagues, research potential employers, read business news, and search and apply for jobs. I keep finding myself asking, though: is LinkedIn too much of a good thing?

Recruiting firms like ours have leveraged LinkedIn for years in order to target specific individuals for specific roles for our clients. Corporations are adopting the same technique, except (in my experience at least) without the amount of rigor necessary to zero in on the proper candidates. Corporations are hiring sourcing teams to scour LinkedIn for talent, resulting in mass messaging to members who may or may not be relevant for the role they have open.

As we found in our flash survey, the rapidly escalating demand for analytic talent means over 60% of you are being contacted at least monthly via LinkedIn. This is generally a wonderful trend for our profession, but my fear is that “recruiter fatigue” may set in. Some of you may adopt a ‘delete all’ policy, scrub your LinkedIn exposure or even remove your profile altogether. This would be an unfortunate consequence of your increased visibility, but I fear this is where we may be headed. My advice is to know what your goals are and prioritize your options accordingly. And of course, to stay in touch with your favorite recruiter!

Your Webinar Questions Answered Part 2

noreply@blogger.com (Linda Burtch) — Wed, 07 Aug 2013 14:51:00 +0000

Thank you again to those of you who attended my webinar presentation on The Burtch Works Study - a comprehensive look at the salaries and demographics of Big Data professionals. In case you missed it, The Burtch Works Study and webinar presentation are available here.

Due to the volume of questions I received during the webinar (we had over 750 registrants) I decided to share some of my insights on my blog in two parts, Part 1 is available here.

10.) Are you seeing salaries for non-Big Data (e.g., market research, smaller scale database or CRM analysts) increasing as a side effect of Big Data demand?

LB: Not yet, but we’ll keep an eye out since we cover those areas too.

11.) I am working in a company that is just beginning to realize the benefits of big data; any suggestions for how to further present the benefits of data and Big Data professionals to a company that so far has not thought it was worth the cost? (Especially how to negotiate salary for data professionals.)

LB: Great question! The payoff for investing in the right Big Data professional is the difference between an ineffective, costly foray into Big Data and a successful and brand-strengthening opportunity. I would highly recommend investigating and presenting specific insights related to your industry. There are new examples every day of companies who are using this wealth of information to their advantage. I would also emphasize that soon the time to gain an edge on the competition will have passed and your company may find itself trying to catch up instead of being a front-runner in the industry. Be diligent about staying abreast of new developments and make sure to network. When you are given the chance, tackle a small project first, rather than going after a big, lengthy project that might not show any results for months. Choose a smaller project where you can quickly get some encouraging reports that will help to show that this is worth investing more time (and money!) in, and allow you more time to go after a bigger, more influential project. As for salary negotiations, a couple of my contacts have already used excerpts from our study as support for the case within their organizations for higher salaries within analytics.

12.) When it comes to recruiting candidates, does age play a role as gender does?

LB: In my opinion it has more to do with attitude, energy, commitment and the ability to change and grow. Companies want to hire someone who is capable of adapting to the changes happening every day in the industry and who will be able to capitalize on new opportunities.

13.) Story Telling is a big part of Analytics in any organization. It is an art and a science...Can a Data Scientist play this role?

LB: Absolutely. Story-telling and strong communication skills are critical for success in analytics. The most successful people in this role can work in a quantitative, technical way as well as selling their ideas to a non-technical audience. I wouldn’t say that one quality is necessarily better to have than the other, but having both makes you a very strong candidate.

14.) There seems to be a lot of tension within and around corporations these days with regards to where the data resides and where it should reside. The data belongs to the company but within the company where does it belong? And if the bulk of a company’s customer-centric data is moved out of IT does this mean the people who work in IT are less valuable or expendable?

LB: You're right in that there's tension in the industry around this issue, mainly because it's become front and center. From my perspective, I think it's extremely important that the relationship between the head of IT and the head of analytics is a strong one. If data is moved out of IT I don’t think that makes IT people more expendable. Anyone that works for a corporation is expendable, and the best way to manage this is to ensure that your skills are up-to-date and marketable in the long run.

15.) Why is it never a good idea to accept a counter offer? I understand that a few people would be annoyed, but isn't that a common outcome?

LB: The number one reason people make the decision to leave is because they want a new challenge or a new environment. Money sometimes plays a part but should not be the only reason. If money is the number one reason you should talk to your boss before going on the market because accepting counter offers is generally frowned upon. It’s a small world and the last thing you want to do is burn any bridges during a transition.

16.) Many of the Analytics Management roles I see posted require deep SAS skills, but do you see growth in roles for those who understand analytics and the applications to solve business problems? Ideally managing a team of analytic professionals but focusing more on strategic goals rather than modeling and being an analytic coach?

LB: I am definitely noticing growth in this area, which I call "Analytics Management" - people who are savvy with numbers and insights, but don't necessarily have the deep modeling and statistical skills of traditional analytics professionals. The most valuable however, can manage, lead and coach with hands-on skills.

17.) I am definitely noticing growth in this area, which I call "Analytics Management" - people who are savvy with numbers and insights, but don't necessarily have the deep modeling and statistical skills of traditional analytics professionals. The most valuable however, can manage, lead and coach with hands-on skills.

LB: I would advise identifying what specifically is making you unhappy so you can possibly come to a resolution with your boss. If a satisfying compromise can’t be made, then perhaps it’s time to update your LinkedIn profile and start looking!

18.) I’ve established myself as a strong candidate and am receiving offers, but I want to make sure that I land the ideal fit: I don't want to get locked into a job that will exhaust me and my family!

LB: It’s a great question because it’s a difficult one, especially when you are in demand! Considering your desire for work/life balance, as general rule avoid consulting, finance or startups and make sure to discuss your concern with your potential hiring manager to get a feel for the company culture. Feel free to reach out us if we can help you evaluate a role, even if it’s not through us!

At Burtch Works we are always looking for trends within the analytics field, so be sure to check back and see if we have posted any new research results!

Your Webinar Questions Answered Part 1

noreply@blogger.com (Linda Burtch) — Fri, 02 Aug 2013 17:54:00 +0000

As many of you know, here at Burtch Works we have been hard at work on The Burtch Works Study - a comprehensive look at the salaries of Big Data professionals. Indicative of the previous scarcity of this information, it has been downloaded over 850 times to date. About a month ago I presented the study via webinar and I am so glad that so many of you could attend! In case you missed it, both the study and the webinar are available for on our website.

Due to the volume of questions (we had 750 registrants) I was unable to answer them all during the Q&A session so I wanted to share a few more insights on some of the other questions I received. I will be sharing these in two parts, so make sure to check back to see the rest of them posted next week.

1.) What would you recommend for a recent graduate who is hoping to become a Big Data professional? What are the most important skills to have?

LB: Securing a statistics or mathematics degree is crucial to becoming a Big Data professional. Since employers know you won’t have a lot of work place experience try to get as much experience with real, messy data sets as you can. Internships offer a great chance to test your skills and can also offer great references on your working capabilities. They can help you figure out what you like! Some computer and government agencies have started to open their data stores, which is a great opportunity for students or beginners to practice. Kaggle offers the chance to compete at solving challenges with real-life data sets, some of them specifically aimed at entry-level job candidates. The more rigorous your quantitative training, the better prepared you will be for the challenges ahead.

2.) Considering the nature of the job, I think skills should play a greater role than X years workplace experience. How are assessments made regarding skill sets?

LB: Skill sets are certainly important in the hiring consideration. Savvy with analytics and Big Data tools such as SAS, R, Hadoop etc. is continuing to be important for analytics professionals at all levels. For junior level candidates you must be able to code and tackle big challenges with these tools. For senior level professionals it is important that you maintain a strong knowledge of these tools so that you can not only mentor your team, but jump in when deadlines are tight (which is becoming increasingly true). There is no magic formula for hiring, but I agree that depth of knowledge is important and discounting a candidate solely based on the years experience criteria is a misguided approach.

3.) I am not sure this is accurate that the bulk of the data scientist talent pool is in west coast. Boston is a huge incubator for Data Scientists.

LB: There is definitely a large pool of what I would call “Data Scientists” working on the west coast with firms who have access to continuous streams of data. You’re right though, that there are other pockets of professionals in other regions including Boston. Firms in Boston tend to focus more on science, insurance and healthcare related industries. Data scientists have been cropping up everywhere – such as Chicago, New York, Dallas, Minneapolis – since the need for them is no longer limited to Silicon Valley.

4.) I would love to know if you have a general cost associated with sponsoring a candidate that needs a Visa transfer. I routinely ask our legal team but they resist sharing the expense with me. It's difficult for me fight for a candidate that is worth the investment when I don't know what the investment is. And certainly my own expectations of a candidate would also be very different if the cost is $2k versus $15k.

LB: From what I’ve been told, the ballpark cost of sponsoring a visa is between $6k and $10k. I know it's not cheap ($2k) nor extremely expensive ($25k+). I covered some information about the OPT/H-1B process in previous a blog post as well as more about the green card process in another blog post. For more about the residency status of quantitative analytics professionals, see this blog post.

5.) Why does the Retail industry pay so low for IC level 2?

LB: It's an interesting question, and I'm not sure that I have an exact answer. This trend holds, though, not only in our study, but also in my experience recruiting for retailers. Generally, retail as an industry is notorious for being extremely tight with expenses due to the very small profit margins. For analytics in particular however, this inclination may be hurting retailers who are trying to compete for Big Data professionals with more competitive tech firms like Amazon and Netflix.

6.) Did you find that salary is related to the name of the university as well? If you graduated from a top 10 graduate school will your salary be higher?

LB: Not necessarily. Although a degree from a big-name school may boost your salary right after you graduate, the effect diminishes over time as your career success becomes the most important indicator for how your company should compensate you. Also if you didn’t graduate from a top school but were successful at a rigorous, quantitative internship that can definitely tip the scales in your favor!

7.) You mentioned that the higher salaries in the Northeast and West Coast don't come close to covering the higher cost of living there. When candidates take new positions and move to these regions are they accepting small pay increases or increases that will cover the higher cost of living? Thank you!

LB: In my experience, we see an average salary increase of 14% across the US and sometimes just above that for individuals in the Northeast and West Coast. We just rarely see a substantial increase, even though quant professionals are often moving from an area of lower to higher cost of living. However Big Data professionals will each have their own ideals when it comes to industry, work environment, compensation and degree of challenge at their job. Money is not the only factor to consider when evaluating a career move.

8.) Is it fair to draw the connection between job descriptions (going down from Data Scientist to Insights Manager) and the levels of IC 1-3 and Mgt 1-3? i.e., are they linked closely enough to assume Data Scientist is IC Level 1?

LB: Very good point! That is why we kept Data Scientists and Market Research individuals i.e. Insights Managers out of the salary pool because they tend to be substantially different than the quantitative professionals. This helped achieve a consistency in results across all levels.

9.) Since the Big Data field is relatively new, how are salaries bench-marked to know what is the right salary to expect for a role? Have your salary survey results been compared to Information Week's annual survey of IT pros or with the self reported numbers on glassdoor.com?

LB: Salary surveys are common in other areas like IT (with reports readily available) but analytics professionals are very different therefore it would be inaccurate to directly compare the two. Glassdoor is also a good resource if you're interested in self-reported salaries from people working at specific companies.

Immigration Reform and Analytics

noreply@blogger.com (Linda Burtch) — Mon, 22 Jul 2013 16:13:00 +0000

Immigration reform has been on the lips of almost every political commentator in the past few weeks. The topic is heavily debated and cumbersome in detail, but as members of the analytic community we must understand it as it affects our job market directly. As we reported in The Burtch Works Study 58.8% of entry-level Big Data professionals are not U.S. citizens, which means that companies are looking abroad for professionals with the necessary skills to tackle their data sets and analytics challenges.

Companies want the best and brightest in the quantitative sciences, and the fact is that right now many of these workers come from outside the U.S. As pointed out by John Shinal in this article that I posted on twitter many companies are building offices overseas when the available visas run out since, “the 65,000 cap on H-1Bs for this year was reached on the very first day that the government began accepting visa applications”. The competition is quite clearly global, and this immigration reform could help boost our standing in the community by encouraging companies to keep their offices here in the U.S.

Rosario Marin, former US Treasurer under President George W. Bush points out in her recent op-ed piece in the Wall Street Journal, “The current skilled-labor shortage—particularly for workers in science, technology, engineering and math occupations—puts U.S. companies at a disadvantage. By 2020, an estimated 1.5 million jobs will go unfilled, according to McKinsey & Co. Until America can educate enough graduates in these fields to meet the demand, legal immigration is the only option to find the necessary talent.” Most of these 1.5 million jobs are in analytics and that number will only continue to grow with further advancements in harnessing Big Data. Even most of the graduate students who excel in quantitative programs at the Master’s and PhD levels in the US come from outside of America. When these students graduate, we need to ensure companies are able to sponsor their visas and keep them in the country lest they find employment opportunities in Europe or Asia.

My hope for any change in the current immigration system is that the United States can continue to provide the best technological and scientific advancements that benefit us every day. Relative to that, as the economy continues its recovery we will see a renewed urgency to hire and fill positions that support such advancements within analytics that didn’t exist ten years ago. This Gallup Poll shows Americans mostly in favor of some type of immigration reform, and though I would not claim that the current bill is the best option, it is certainly a starting point.

Data Scientists . . . or Data Wannabes?

noreply@blogger.com (Linda Burtch) — Wed, 12 Jun 2013 18:31:00 +0000

Ladies and gentlemen of the analytic community – we have an epidemic. A seed has been planted in the minds of quantitative professionals that has sprouted and will not subside. The more we hear about Big Data, the more we hear about ‘Data Scientists.’ And like with any hot job, once the media starts to speculate on salary, candidates go nuts. As the President of Teradata Scott Gnau said in a recent article about careers in Big Data, “There are a lot of people who can spell Hadoop and put it on their resume and call themselves data scientists.”

Gnau makes a plea to get the term ‘Data Scientist’ defined and I could not agree more. When a national article makes the claim that one of these workers can be making $200K+ with a couple of years of experience, you can bet everybody who works with data will start calling themselves a Data Scientist. I have already seen it start to happen and thought I would offer my two cents on the reality of this highly coveted (and grossly overused) title.

The compensation issue is a tough one to tackle. Probably the most common question I get asked as a recruiter, by both candidates and clients, is related to salary. While it’s true that careers in marketing analytics are in demand and lucrative, this is all dependent on multiple factors including: years of experience; software familiarity; advanced degrees; pedigree of school; location preferred; and many more. So when Rob Bearden, the CEO of Hortonworks, says that a “qualified data analyst” coming right out of school can make $125K, note that this is a very specific candidate with a very impressive background. To name just a few bullet points of the perfect candidate, data scientists typically have:

A PhD in Computer Science and advanced degrees in other highly quantitative disciplines like Mathematics
Knowledge of MapReduce tools like Hadoop
Use of tools such as Python, R, Java, Hive, Pig
Prior work or internship experience working with enormous, unstructured data sets

These are just general guidelines, of course. To be sure, a candidate with a Masters in Statistics can be a Data Scientist but this candidate will likely not be demanding as high of a salary as someone with two PhDs and Google on his resume. It might help to think of Data Scientists as a unique subset of the Big Data analytics profession. Just as an IT professional may work with Big Data, a marketing analyst who builds predictive models is not necessarily a Data Scientist.

Allow me to reiterate what I’m sure you have already heard countless times in the media: careers in Big Data are in demand and continue to grow. However, not every person with a quantitative background or experience in analytics is a real Data Scientist. The tools they are using are very new and many companies looking to take advantage of Big Data do not have a previously established foundation of Data Scientists to provide road maps for less experienced professionals. Likewise, innovative companies like Hortonworks that work to support tools such as Hive and HBase can and must offer workers competitive salaries to ensure the use of these systems are further developed.

For now we will continue to see ‘Data Scientist’ placed on resumes to catch the attention of hiring managers, and hiring managers will continue to shell out unprecedented resources to attract talent. Rightfully so – these guys are one in a million.