By Derek Steer, Mode Analytics.
The data Science Venn Diagram puts forth three areas of technical proficiency for data science: math & statistics knowledge, substantive expertise, and hacking skills. Each area will be necessary at some level for every data scientist — I’ve seen plenty of companies over-index on one area at the expense of the other two, and it never ends well.
For instance, a data scientist focused mostly on research will likely need to be much stronger in math and statistics than she will in hacking skills. But she’ll still need those hacking skills, as they will play a critical role in finding opportunistic ways to optimize the systems used in their research. A data scientist tasked with implementing new product features that use data science as part of their function will need to be much deeper in hacking skills, so that they can go about managing the implementation of those new features. But they’ll still need a deep understanding of statistics, so that the math behind those features produces the best possible outcomes. And a data scientist working in a field with niche data types and esoteric processes, like healthcare, will require much more substantive expertise. But without math and programming depth, they won’t be able to put that expertise to use.
There’s another side to this coin as well; you shouldn’t interview a candidate for technical skills they will never use in the role. Someone with deep knowledge of machine learning may be attractive on paper. But if that’s not part of the job, then prioritizing that candidate may result in a pass on someone who would have been great in the role. Or worse, you may hire someone whose expectations don’t align with reality, and have to go back to square one when they leave after just a few months.
Each area will be necessary at some level for every data scientist. Structure your technical interview to explore all three with a problem-solving question for each, at the level of technical depth necessary to be successful. Due to the changing nature of technology and the speed at which languages come and go, these questions should be built such that they can be approached without specific stack experience. Technical questions should be challenging, and having questions with multiple stages is often helpful to see just how deep someone can get into a problem.
These ideas should provide a framework for interviewing data scientist candidates. The nature of this field means there’s no one-size-fits-all interview structure. But you can use the three focus areas above to build your own, with the confidence that you’re covering the bases you need to ensure a good fit.
Original. Reposted with permission.
Bio: Derek Steer is CEO of Mode Analytics.
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