Webinar discussion: How do you evaluate data science candidates for roles?

Continuing the discussion from the Building Effective Data Science Teams Webinar:

How do you evaluate data science candidates for roles?

Some discussion from the webinar:

Our panelists for this webinar were:

  • Kobi Abayomi, Senior VP of Data Science at Warner Music Group
  • Gregory Berg, VP of Data Science at Caliber Home Loans
  • Elaine McVey, VP of Data Science at The Looma Project
  • Jacqueline Nolis, Head of Data Science at Saturn Cloud
  • Nasir Uddin, Director of Strategy & Inspirational Analytics at T-Mobile
  • Moderated by Julia Silge, Software Engineer at RStudio, PBC

Greg: There are a couple of things. One is what their academic background is, depending on what I’m after. I don’t want to just hire people like me, as economists. I need a broader skill set.

The other is their skills and things that they’ve worked on. You can get a sense of how technical they are in certain areas just based on that. When it comes to a phone interview or talking to them, I shift. I talk about some technical things, but I shift into the behavioral-based interviewing. I want to know how they perform and what they did in different specific situations with the idea being that if they perform this way in a certain situation at a company, that could carry over. So, I have the dual frame of technical and behavioral based interviewing.

Kobi: I like what Greg just said. These days, I try to divine out what they actually do and what was created beyond the jargon on the resume. When I’m looking at resumes, I look for that same narrative and description that illustrates an understanding of what was done. I don’t fault candidates for this, but part of this is the nature of the way people are finding jobs and hiring managers are finding resumes.

There’s a tendency to throw a lot of the jargon at NLP behind the resume. You’ll see resumes with a list of acronyms of the software they use and a list of types. I’ve seen people break out clustering analysis and then list types of clustering analysis - machine learning - and then they’ll list types of machine learning. I’ve found, more often than not, candidates behind those resumes didn’t have the level of a sophisticated understanding that I’m looking for - problem solving. Technology will change and techniques will change. I look for people who can think and are willing to. With this field these days, it’s difficult to parse it out, but I think what Greg said about having a scenario and that sort of discussion is helpful.