Executive Edge: What makes a good data scientist?
Four things to look for when building an analytics team.
By Andrew Jennings
Companies in every industry from retail to banking are leveraging big data to improve the customer experience and enhance their bottom lines. Big data – high volume, high velocity (real time) and high variety (structured and unstructured) data – is transforming the way we live and conduct business across all industries and all aspects of daily life.
This has created a talent gap for qualified data scientists. And this is not purely a Silicon Valley tech phenomenon. Gartner estimates that big data will generate six million new U.S. jobs in the next three years, including non-technical roles (see CNNMoney).
Last October a Harvard Business Review article called data scientist “the sexiest job of the 21st century,” and Indeed.com reported that job postings for analytic scientists have jumped 15,000 percent between the summer of 2011 and 2012. McKinsey & Company predicted a 50 percent to 60 percent shortfall in analytic scientists in the United States by 2018. Gartner echoed this sentiment, predicting that only one-third of 4.4 million global big data jobs will be filled by 2015.
Prior to 2000, the analytics function, outside of a few places like retail banking, was relegated to the finance or IT department. Now, many companies are hiring autonomous analytics teams that work across departments. There is no magic to leveraging big data in pursuit of solutions to business problems. Yes there is the technology – sophisticated predictive analytics, for example – but at the heart of a successful deployment is still human intelligence. Hiring the right people is crucial.
So what makes a good data scientist? What qualities should a company look for when recruiting and interviewing candidates?
I’ve been with FICO for 20 years, and the company itself has been hiring data scientists (by any name) since 1956. We’ve hired some of the best – and probably a few who should never have been let near a data set. Here’s what we believe you should do when building your own analytics team.
1. Find people who are focused on solving problems, not just boosting model performance curves. Math skills are important, but the point of leveraging big data analytics is solving business problems. It’s coming up with answers to challenges that will actually be useful in the real world. It means answering specific questions in ways that will be helpful to the bottom line. For example, key questions would include: What decision are we looking to improve? How will we measure the improvement? How do we make that decision today? What are the deployment constraints? And so on. These are all practical questions before one gets to the data and the statistical techniques, which are generally the things that attract all the media attention.
One example of a big data challenge that seems to resonate universally and helps highlight the importance of these questions is customer attrition. Most businesses are focused on retaining their best customers. Aside from thorny questions like what does “best” mean, there are other important questions such as, how far ahead of the potential attrition event does the prediction need to be made? In other words, how does one construct the problem to allow time between the prediction indicating an attrition risk and the delivery of some action and that action having enough time to be effective? These are business context questions that need to be answered long before a data analyst can be effective.
2. Make sure they can talk with people who don’t hold Ph.D.s. Data scientists are not simply good problem solvers; they are also good at helping to identify the right problems to solve and framing the questions in such a way as to yield meaningful answers. The challenges whose solutions have the most value to an organization are not easy to solve and they often take a non-mathematical mindset. How can we make changes for the better? Where do we even start?
Some data scientists are abstract thinkers who are technical and academic. And then there is a rare breed, those data scientists who can think and conceptualize and communicate to a business audience. Given some of the key questions above, ideally you want an individual who is business-savvy and well-versed enough on the larger strategy that he or she can have a discussion with the business user. If you could only choose one person this profile would be the perfect package, but these individuals are hard to find.
In a team context, making that trade-off between best-in-class technical skills and strong communicators who can help translate the highly technical information into language that a business user can understand is a trade-off worth making. Also, going in reverse, those same people need to be able to translate a business need into an analytics investigation.
Ideally, even if the back-office analytics folks won’t speak to clients, you want them to want to, because that indicates that they’re thinking of things from a client perspective, not just a technology perspective. There are some data scientists who will never want to move beyond an R&D role, and for these folks, communications may seem less important – but then again, don’t you want them to be able to justify their work, explain its benefits and author white papers?
3. Put more emphasis on skills and mindset than degrees. Clearly, a strong background in numerical science is a necessity. Not all candidates need to be a Ph.D. in mathematics or operations research; they may be electrical engineers or sociologists. I have become less concerned about those specifics and far more attuned to the mindset. Good data scientists are not only technically sound, with attention to detail, but they are also inquisitive and open-minded; they question everything that they find. They ask tough questions of the data and equally of the veracity of the conclusions. Big data doesn’t guarantee the right answer. People still need to think about getting to the right answer.
Increasingly, the effective data scientist needs to be able to automate. This means that they need to be comfortable with writing scripts and code to make their work efficient, mixing and matching tools, and have the ability to absorb new techniques.
From a long-term career perspective, one of the big opportunities is that data science can lead in any number of directions. Some end up in sales, finance or executive management. Others start off in more traditional corporate roles and slowly gravitate toward jobs that are more heavily steeped in predictive analytics. A broad skill set always comes in handy, and there will ultimately be a range of opportunities where an analytic mindset can be applied effectively. Being inquisitive goes a long way.
For those looking to transition to a data analytics role from, for example, a financial or economics background, basic programming skills are important. Being able to manipulate data and think logically will impress hiring managers. They will likely want to see a demonstrated ability to learn a programming language, and link various concepts via code. There is obviously a need for individuals who are well-versed in big data programming frameworks such as Hadoop and statistical programming languages such as R.
4. Use your current analysts to sniff out the real data scientists from the pretenders. As more and more candidates start self-identifying as data scientists, sorting through them all has become more challenging. When screening and interviewing data scientists, having one or more involved in the process, someone who really knows what he or she is doing, is an obvious part of the recruitment process. This is particularly important for those hiring managers with a traditional business background who may not know the right questions to ask.
Some candidates will of course over-represent their background and experience. They may claim to have run a full analytics process but really only have been involved in part of it. You don’t want to hire someone who says they’re a modeling superstar but in fact has been specializing in data cleansing. In all the buzz, analytics has become a broadly and loosely used term. They may know the lingo, but if they are not familiar with how a whole analytic project is put together, the knowledge gaps may be too great to overcome.
A word of caution: The best analytic teams will embrace diversity of experience and skills. Just like any other hiring situation you always need to guard against hiring people that “look just like the people you already have.”
We’re entering a new age of analytic competition. It’s a great time to be a data scientist, but a tricky time to hire one. Every candidate will claim mad math skills – your job is to appraise those while also looking for the problem-solvers, the communicators and the skills that will make your data scientists a more valuable part of your whole organization.
Andrew Jennings is the chief analytics officer of FICO. He is a member of INFORMS.
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