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 Director of Data Science at LinkedIn, where he leads a team straddling the boundary between science and engineering and delivering products that LinkedIn’s 187M+ members love and use everyday. He previously led a local search team at Google, and he was a founding employee of faceted search pioneer Endeca (acquired by Oracle in 2011), where he spent ten years as Chief Scientist.
Part of his mission is bridging the gap between industry and academia. He established the Symposium on Human-Computer Interaction and Information Retrieval (HCIR), which has taken place annually since 2007. In addition to speaking at two previous Strata conferences, he has organized and delivered invited talks at top international conferences focused on search, recommender systems, and social media. He wrote the first book on faceted search as part of the Morgan & Claypool Synthesis Lectures, and he is on the editorial board of the Journal of Big Data.
He holds degrees in math and computer science from MIT and a PhD in computer science from CMU, where he developed new algorithms for network visualization. He blogs at The Noisy Channel.
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