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How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
How to Interview a Data Scientist
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How to Interview a Data Scientist

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How To Interview a Data Scientist …

How To Interview a Data Scientist
Daniel Tunkelang

Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI

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.

Published in: Technology
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  • https://www.youtube.com/watch?v=gUTuESHKbXI
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  • Daniel: I see that you have a lot of publication of high value. All the more reason why I would like you to take a look at my (somewhat primitive proposals). Links posted in an earlier comment on Design for Interaction.
    Thanks and regards,
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  • When I interview for jobs or college applications one of my favorite questions is, 'What's the hardest problem you have ever solved?' Not only do I get to see the depth of their experience but the insight I get from how well they describe the hard problem is more valuable than any coding trivia question. And sometimes I learn something interesting from the discussion, too.
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  • Timothy, I'm sorry for your negative experience. But ask anyone who has interviewed specifically for my team (there are multiple data science teams at LinkedIn), and you'll see that I practice what I preach. It's not a matter of kool-aid -- it's just good interviewing. We have done some whiteboard coding, but that's typically only a small portion of the onsite and mostly happens in a phone screen using collabedit.

    In any case, I appreciate your feedback -- I'll share it with the other hiring managers.
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  • I was asked almost nothing but gotcha' questions when being interviewed as a Data Scientist by three other Data Scientists at LinkedIn. And that's after being introduced by a colleague, a former Data Sicntist there, who had just left to head up LinkedIn's Research Lab!

    Plus, one of these Data Scientists wasn't even on-site that day and not only asked me to write SQL code but expected me to to do it verbally, worse than being asked to code on the white-board.

    As usual, it's easier to say you drink your own kool-aid than it is to actually drink it!

    TV
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  • 1. DanielHow to Interview a Data ScientistDaniel TunkelangDirector of Data Science, LinkedIn Recruiting Solutions 1
  • 2. Drew Conway’s Venn Diagram 2
  • 3. GOAL 3
  • 4. Specification for a Data Scientist implements algorithms analyzes data thinks product 4
  • 5. What aboutC ulture ommunication uriosity Hold that thought… ? 5
  • 6. What can you learn from an interview? 6
  • 7. Interviewing is a last resort. Alternatives? 7
  • 8. Only hire people you’ve worked with. 8
  • 9. Hire interns. Convert to full-time. Profit! 9
  • 10. Try before you buy: short-term contracts. 10
  • 11. Alternatives are at best a partial solution.§  Only hiring people you’ve worked with doesn’t scale. –  And traps you in a locally optimal monoculture.§  Interns are great! But they are a significant investment. –  Managing interns well is a productivity gamble. –  Most interns have at least a year of school left. –  Not all interns will make your bar. You won’t always make theirs.§  Try before you buy: nice in theory. –  Adverse selection bias when other offers are permanent roles. –  Creates bureaucracy. 11
  • 12. Can we at least make interviews natural? 12
  • 13. Spend a day working together. 13
  • 14. Take-home assignment. 14
  • 15. Review candidate’s previous work. 15
  • 16. High-fructose corn syrup is 100% natural.§  Working sessions are difficult to set up. –  No more natural than a final exam. –  High variance, and very difficult to calibrate performance.§  Take-home assignments are great for the employer. –  But they are a significant investment for the candidate. –  Adverse selection bias if other companies don’t require them. –  Creates incentive to cheat if significant part of hiring process.§  Previous work is like natural experiments. –  Always good to review a candidate’s previous work. –  But not always possible to find work with high predictive value. 16
  • 17. So you gotta do interviews. But how? 17
  • 18. Three Principles1.  Keep it real.2.  No gotchas.3.  Maybe = no. 18
  • 19. Keeping It Real 19
  • 20. Test basic coding with FizzBuzz questions. multiple of 3 -> Fizz multiple of 5 -> Buzz multiple of 15 -> FizzBuzz 1, 2, Fizz, 4, Buzz, Fizz, 7, 8, Fizz, Buzz, 11, Fizz, 13, 14, FizzBuzz, 16, … 20
  • 21. Whiteboards suck for coding. http://ericleads.com/2012/10/how-to-conduct-a-better-coding-interview/ 21
  • 22. Don’t ask pointless algorithm questions. implement 22
  • 23. Use real-world algorithms questions. bigdatascientist Did you mean: big data scientist 23
  • 24. Ask candidates to design your products. 24
  • 25. Keeping it real is also a great sell. Similar Profiles People You May Know 25
  • 26. But no gotchas. 26
  • 27. Gotchas reduce the signal-to-noise ratio.§  Avoid problems where success hinges on a single insight. –  Good interview problems offer lots of room for partial credit. –  Making a key insight often reflects experience, not intelligence.§  Don’t test a candidate’s knowledge of a niche technique. –  Unless that niche technique is critical to job performance. –  And can’t be learned on the job as part of on-boarding.§  Be a hard interviewer, but don’t be an asshole. –  An interview is not a stress-test to see where candidates break. –  Interviews communicate your values to the candidate. 27
  • 28. Maybe = no. 28
  • 29. Commit to binary interview outcomes.§  Forced choice so interviewers don’t take easy way out. –  Just like having 4 choices instead of 5 on a rating scale. –  Encourages interviewers to take their role seriously.§  Each team member is a critical filter. –  Two no’s or one strong no is a no. –  All weak yes’s is a no.§  Short-circuit candidates early in the process. –  Resume and phone screening should be aggressive. –  Onsite interviews should have ~50% chance of leading to offers. 29
  • 30. But what aboutC ulture ommunication uriosity All are must-haves. ? Every interview evaluates all three. 30
  • 31. Remember Your Goal 31
  • 32. Three Principles1.  Keep it real. –  Avoid whiteboard coding. Filter with FizzBuzz. –  Use real-world algorithms questions. –  Ask candidates to design your products.2.  No gotchas. –  Gotchas reduce the signal-to-noise ratio.3.  Maybe = no. –  Bad hires suck. Be conservative. –  Trust your team. 32
  • 33. Thank you! 33

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