Tag Archives: job titles

Building a Complementary Data Science Team

This is an updated version of an article first posted on Medium on Nov. 23, 2015. I’ve disabled the links to the jobs, as those specific ones are no longer available. If you’re interested in a role at EAB or the Advisory Board, please get in touch, though!


I’m the Director of Data Science at EAB, a firm that provides best-practices research and enterprise software for colleges and universities. My team is responsible for the predictive models and other advanced analytics that are part of the Student Success Collaborative product that’s used by academic advisors and other campus leadership. We’re hiring data scientists, and I wanted to publicly say a few things about the roles we have advertised. (Note that EAB is part of a public company and is in a competitive market, so there are obviously things I’m not saying!)

The most important point is that data scientists specialize, so look for the specializations. My co-authors and I made this point in our 2012 e-book Analyzing the Analyzers, and the folks at Mango Solutions are burning up Twitter with their self-service tool for identifying data science strengths and weaknesses.

Drew Conway’s Data Science Venn Diagram

A related point is that existing framing devices can help you balance a team. Drew Conway’s Venn Diagram remains a great way to think about Data Science aptitude. Combine people with strengths in each part of the diagram, who know enough to collaborate effective and make each other stronger, and you don’t need a team of unicorns with 3 PhDs each.

I suspect the details of the framing device are less important than the fact that you have one. It forces you to think about variety and complementary skills, and how people work together to solve problems and build systems.


At EAB, we have four career tracks for data scientists — Research, Engineering, Statistical Programming, and Management. Our new roles supplement our existing team by adding several new people, each with different capabilities and seniority.

At a Senior level, we’re looking for a Statistical Programmer-track person who is particularly strong in algorithm development and implementation, perhaps a straight-up Computer Scientist. Think of the “Machine Learning” area in Drew’s diagram. As we look to expand the classes of statistical techniques that we use, we need more people who know the academic literature and can figure out exactly what technical solution will let us build and scale high-quality models. Interested? Please apply!

A little less senior, we’re also looking for a Researcher who can help us apply domain knowledge even more effectively in our analyses, models, and systems. Some software, data visualization, and statistical skills required — maybe a quantitative Social Scientist pivoted into industry? The upper edge of the Substantive Expertise area. Sound like you? Please apply!

I strongly believe that a Data Science team should do all of the Data Science, including building and owning models in production. So, last but not least, we’re looking for another Engineering-focused data scientist, who can help us build model frameworks, data tools, workflow tools, and more. This role can be junior or even entry-level, but we do need programming skills, statistical thinking skills, and some sort of portfolio. Programmer and recent data science boot camp grad, perhaps? Please apply!

Of course, as we talk to people, learn what they’re good at and excited about, and what they bring, we may end up with a different mix of skills. But regardless, they’ll cover the space of data scientists, will provide different perspectives and skills, and will help us own our own tools and systems so that we can move and learn quickly.

On “Analytics” and related fields

I recently attended the INFORMS Conference on Business Analytics and Operations Research, aka “INFORMS Analytics 2011”, conference in Chicago. This deserves a little bit of an explanation. INFORMS is the professional organization for Operations Research (OR) and Management Science (MS), which are terms describing approaches to improving business efficiency by use of mathematical optimization and simulation tools. OR is perhaps best known for the technique of Linear Programming (read “Programming” as “Planning”), which is a method for optimizing a useful class of mathematical expressions under various constraints extremely efficiently. You can, for example, solve scheduling, assignment, transportation, factory layout, and similar problems with millions of variables in seconds. These techniques came out of large-scale government and especially military logistics and decision-making needs of the mid-20th century, and have now been applied extensively in many industries. Have you seen the UPS “We (heart) Logistics” ad? That’s OR.

OR is useful, but it’s not sexy, despite UPS’ best efforts. Interest in OR programs in universities (often specialties of Industrial Engineering departments) has been down in recent years, as has been attendance at INFORMS conferences. On the other hand, if you ignore the part about “optimization” and just see OR as “improving business efficiency by use of mathematical processes,” this makes no sense at all! Hasn’t Analytics been a buzzword for the past few years? (“analytics buzzword” gets 2.4 million results on Google.) Haven’t there been bestselling business books about mathematical tools being used in all sorts of industries? (That last link is about baseball.) Hasn’t the use of statistical and mathematical techniques in business been called “sexy” by Google’s Chief Economist? How could a field and an industry that at some level seems to be the very definition of what’s cool in business and technology right now be seen as a relic of McNamara’s vision of the world?

To answer that rhetorical question, I think it’s worth considering the many ways that organizations can use data about their operations to improve their effectiveness. SAS has a really useful hierarchy, which it calls the Eight levels of analytics.

  1. Standard Reports – pre-processed, regular summaries of historical data
  2. Ad Hoc Reports – the ability for analysts to ask new questions and get new answers
  3. Query Drilldown – the ability for non-technical users to slice and dice data to see results interactively
  4. Alerts – systems that detect atypical conditions and notify people
  5. Statistical Analysis – use of regressions and similar to find trends and correlations in historical data
  6. Forecasting – ability to extrapolate from historical data to estimate future business
  7. Predictive Analytics – advanced forecasting, using statistical and machine-learning tools and large data sets
  8. Optimization – balance competing goals to maximize results

I like this hierarchy because it distinguishes among a bunch of different disciplines and technologies that tend to run together. For example, what’s often called “Business Intelligence” is a set of tools for doing items #1-#4. No statistics per se are involved, just the ability to provide useful summaries of data to people who need in various ways. At its most statistically advanced, BI includes tools for data visualization that are informed by research, and at its most technologically advanced, BI includes sophisticated database and data management systems to keep everything running quickly and reliably. These are not small accomplishments, and this is a substantial and useful thing to be able to do.

But it’s not what “data scientists” in industry do, or at least, it’s not what makes them sexy and valuable. When you apply the tools of scientific inquiry, statistical analysis, and machine learning to data, you get the abilities in levels #5-#7. Real causality can be separated from random noise. Eclectic data sources, including unstructured documents, can be processed for valuable predictive features. Models can predict movie revenue or recommend movies you want to see or any number of other fascinating things. Great stuff. Not BI.

And not really OR either, unless you redefine OR. OR is definitely #8, the ability to build sophisticated mathematical models that can be used not just to predict the future, but to find a way to get to the future you want.

So why did I go to an INFORMS conference with the work Analytics in its title? This same conference in the past used to be called “The INFORMS Conference on OR Practice”. Why the change? This has been the topic of constant conversation recently, among the leaders of the society, as well as among the attendees of the conference. There are a number of possible answers, from jumping on a bandwagon, to trying to protect academic turf, to trying to let “data geeks” know that there’s a whole world of “advanced” analytics beyond “just” predictive modeling.

I think all of those are right, and justifiable, despite the pejorative slant. SAS’ hierarchy does define a useful progression among useful analytic skills. INFORMS recently hired consultants to help them figure out how to place themselves, and identified a similar set of overlapping distinctions:

  • Descriptive Analytics — Analysis and reporting of patterns in historical data
  • Predictive Analytics — Predicts future trends, finds complex relationships in data
  • Prescriptive Analytics — Determines better procedures and strategies, balances constraints

They also have been using “Advanced Analytics” for the Predictive and Prescriptive categories.

I do like these definitions. But do I like the OR professional society trying to add Predictive Analytics to the scope of their domain, or at least of their Business-focused conference? I’m on the fence. It’s clearly valuable to link optimization to prediction, in business as well as other sorts of domains. (In fact, I have a recent Powerpoint slide that says “You can’t optimize what you can’t predict”!) And crosstalk among practitioners of these fields can be nothing but positive. I certainly have learned a lot about appropriate technologies from my membership in a variety of professional organizations.

But the whole scope of “analytics” is a lot of ground, and the underlying research and technology spans several very different fields. I’d be surprised if there were more than a dozen people at INFORMS with substantial expertise in text mining, for example. There almost needs to be a new business-focused advanced analytics conference, sponsored jointly by the professional societies of the machine learning, statistics, and OR fields, covering everything that businesses large and small do with data that is more mathematically sophisticated (though not necessarily more useful) than the material covered by the many business intelligence conferences and trade shows. Would that address the problem of advanced analytics better than trying to expand the definition of OR?

“Data Scientist” and other titles

Neil Saunders has an interesting (to me) blog post up this morning, with the title “Dumped on by data scientists.” He uses the use of “data scientist” in a Chronicle of Higher Ed article to rant a little bit about the term. For Neil, it’s redundant, as the act of doing science necessarily requires data; it’s insulting, as if “scientist” wasn’t cool enough and you have to add “data”; and it’s misleading, as many people who call themselves “data scientists” are actually dealing with business data rather than scientific data.

Without disagreeing that there’s a terminological sprawl going on, I did want to address the use of the term, and partially disagree with Neil.

As someone with scientific training who uses those tools to solve business problems, I certainly struggle with a description of my role. “Data Scientist” or “Statistical Data Scientist” is actually pretty good, as it correctly indicates that I use scientific techniques (controlled experiments, sophisticated statistics) to understand our company’s data. I often describe myself as a “Statistician”, too, which gets across some of the same ideas without people having to do a double take and parse a new phrase. I also sometimes describe myself as doing “Operations Research” (aka “Management Science”, although I don’t use that term), since I use some of the tools of that field, as well as of Artificial Intelligence/Machine Learning, to optimize certain objective functions.

“Business Intelligence” actually is not that good a term for what I do, as most of what is usually called BI is about tools for better/more relevant/faster access to data for business people to use. This is not a bad thing to be doing, at all, but it’s different from the predictive and inferential statistical methods that I use in my job.

I don’t know what the right answer is. It might depend on the precise person and their precise role. My title, for instance, is the result of a back-and-forth with my boss, HR, and others, trying to find words that have both appropriate internal and external meanings. “Technical Lead” is a rank, indicating that I run technical projects without (formally) managing people. “Inventory Optimization and Research” covers a variety of areas. “Inventory” here means “sellable units”, like boxes on a shelf, or in this case, like scheduled airline flights. Probably baffling for an external audience without an explanation, but extremely clear inside the company. “Optimization” means what it sounds like, both in a technical and a non-technical sense, and for both internal and external audiences. “Research” indicates a focus on the development of long-term and cutting-edge systems. “Data Scientist” didn’t end up in there, but it could have.

For people using Big Data tools and scientific methods to study topics inside academia, the right answer seems to me to put the field of study first. You’re not a “Data Scientist”, you’re an astrophysicist, or a bioinformatician, or a neuroscientist, with a specialization in statistical methods. If you’re a generalist inside the academy, you’re probably a statistician. Perhaps “Data Scientist” should be restricted to people applying scientific tools and techniques to problems of non-academic interest? That might work, as long as it included people who do things like apply predictive analytic tools to hospital admissions data.