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How to Interview a Data Scientist

Daniel Tunkelang Director of Data Science, LinkedIn

Daniel

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Drew Conway’s Venn Diagram

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GOAL

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Specification for a Data Scientist

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analyzes data

implements algorithms

thinks product

What about

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C ulture ommunication

uriosity ?

Hold that thought…

What can you learn from an interview?

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Interviewing is a last resort.

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Alternatives?

Only hire people you’ve worked with.

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Hire interns. Convert to full-time. Profit!

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Try before you buy: short-term contracts.

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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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Can we at least make interviews natural?

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Spend a day working together.

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Take-home assignment.

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Review candidate’s previous work.

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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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So you gotta do interviews. But how?

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Three Principles

1.  Keep it real.

2.  No gotchas.

3.  Maybe = no.

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Keeping It Real

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Test basic coding with FizzBuzz questions.

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1, 2, Fizz, 4, Buzz, Fizz, 7, 8, Fizz, Buzz, 11, Fizz, 13, 14, FizzBuzz, 16, …

multiple of 3 -> Fizz multiple of 5 -> Buzz multiple of 15 -> FizzBuzz

Whiteboards suck for coding.

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http://ericleads.com/2012/10/how-to-conduct-a-better-coding-interview/

Don’t ask pointless algorithm questions.

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implement

Use real-world algorithms questions.

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bigdatascientist

Did you mean: big data scientist

Ask candidates to design your products.

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Keeping it real is also a great sell.

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Similar Profiles

People You May Know

But no gotchas.

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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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Maybe = no.

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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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But what about

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ulture ommunication

uriosity ?

All are must-haves. Every interview evaluates all three.

C

Remember Your Goal

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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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Thank you!

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