1. Daniel
How to Interview a Data Scientist
Daniel Tunkelang
Director of Data Science, LinkedIn
Recruiting Solutions 1

2. Drew Conway’s Venn Diagram
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3. GOAL
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4. Specification for a Data Scientist
implements
algorithms
analyzes data
thinks product
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5. What about
C ulture
ommunication
uriosity
Hold that thought…
?
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6. What can you learn from an interview?
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7. Interviewing is a last resort.
Alternatives?
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8. Only hire people you’ve worked with.
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9. Hire interns. Convert to full-time. Profit!
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10. Try before you buy: short-term contracts.
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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.
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12. Can we at least make interviews natural?
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13. Spend a day working together.
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14. Take-home assignment.
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15. Review candidate’s previous work.
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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.
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17. So you gotta do interviews. But how?
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18. Three Principles
1. Keep it real.
2. No gotchas.
3. Maybe = no.
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19. Keeping It Real
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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, …
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21. Whiteboards suck for coding.
http://ericleads.com/2012/10/how-to-conduct-a-better-coding-interview/
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22. Don’t ask pointless algorithm questions.
implement
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23. Use real-world algorithms questions.
bigdatascientist
Did you mean:
big data scientist
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24. Ask candidates to design your products.
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25. Keeping it real is also a great sell.
Similar Profiles
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26. But no gotchas.
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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.
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28. Maybe = no.
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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.
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30. But what about
C ulture
ommunication
uriosity
All are must-haves.
?
Every interview evaluates all three.
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31. Remember Your Goal
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32. Three Principles
1. 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.
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33. Thank you!
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