New Blog Site

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

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

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?

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

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

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

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

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

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

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

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?

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.