Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Daniel Tunkelang is the CTO of Lyra Health, a company working to transform mental health care. He previously was a director of data science and engineering at LinkedIn, and he led a local search quality team at Google. He was a founding employee of Endeca, which was acquired by Oracle in 2011. He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has spoken at four previous Strata conferences, and is on the editorial board of the Journal of Big Data. He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
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