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How to Interview a Data Scientist


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How To Interview a Data Scientist
Daniel Tunkelang

Presented at the O'Reilly Strata 2013 Conference

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
  • Nice !! Download 100 % Free Ebooks, PPts, Study Notes, Novels, etc @
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  • it's really good for me to have a look in this slide before i go to my interview session later, thanks a lot
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  • @Daniel Tunkelang I think that in every technical field this is done through the expectation of the organization (Or our perception of the expectation) on what is required when we interview folks. As we all know there is little value of evaluating people based on these false environment (White Board on Demand) to perform a certain technical or procedural exercise. There times when even the most knowledgable of us in a specicifc field will need to solidify our knowledge on something, trivial in the field, that we have not used in a while, so we may need to verify. So in summary I think there are two parts, one is doing what is expected of us as interviewers (usually from training or environment) and doing what is practical and make sense. There is immediate mental access and proficiency required for certain task but one should always ask if this immediate access part of the critical underpinning of understanding if an interview exercise evaluates relevancy to a person's knowledge set and ability to perform a role. Good Post topic thanks!!!!!
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How to Interview a Data Scientist

  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. 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