Balancing strengths

I was once asked in an interview about the classic data science Venn diagram, “Of the three categories, which one is your biggest strength?” They were visibly surprised by my answer, “domain knowledge”. A lot of data scientists emphasize their coding skills, and I was eager to talk about the business. 

Now that I’ve hired a fair number of data scientists, I think about the utility of this question. It’s an effective exercise in balancing strengths on a team. The goal is to hire well-rounded professionals, but eventually, their skills shine through and that’s a good thing! Here are common signals that highlight those strengths for you.

MATH AND STATS

I studied math in college and sophomore year, I chose statistics as my minor. I loved stats! I felt so lucky to have found the perfect complement to my major. It’s this excitement for the algorithm details and strong adherence to experimentation design that point to the math and stats talent.

COMPUTER SCIENCE

Does a data science solution require object oriented programming? Sometimes! And how great is it when that expertise is available to harness. Usually you’d hire a machine learning engineer if you need to implement in a platform environment. But if your candidate perks up describing the package they built last weekend, then coding clearly has the advantage in this case.

DOMAIN KNOWLEDGE

The ability to find creative solutions comes from a deep understanding of the domain. It’s a necessary skill in the field of analytics, and expressing passion from experience in a similar industry is one way to communicate that domain expertise.

Bringing a wide range of skills into the team lifts everyone’s abilities as data scientists. Peer review, mentorship, and pair programming are a few team experiences that improve with varied strengths in the data science skill set. Target specific strengths when you’re hiring to create a balance and the entire team will benefit.