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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
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What about
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C ulture ommunication
uriosity ?
Hold that thought…
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What can you learn from an interview?
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Interviewing is a last resort.
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Alternatives?
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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
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Whiteboards suck for coding.
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http://ericleads.com/2012/10/how-to-conduct-a-better-coding-interview/
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Don’t ask pointless algorithm questions.
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implement
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Use real-world algorithms questions.
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bigdatascientist
Did you mean: big data scientist
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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
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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.
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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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