Thread by @v_vashishta

Most data science interview questions are trash and don't do a good job assessing capabilities.

I have hired over 100 data scientists. Here's my framework for creating and asking effective interview questions.
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#DataScience #MachineLearning #jobinterview

A competent Data Scientist wants focused, intelligent, high level interview questions. We are comfortable with interpreting, synthesizing, and providing our perspective. It’s a core capability.
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#DataScience #MachineLearning #jobinterview

In an interview, that comes through in our answers to well thought out questions. We are looking for an opportunity to showcase our experience. We want to talk about what we’ve built and the results we’ve produced.
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#DataScience #MachineLearning #jobinterview

We are better suited to talking about how we apply foundational concepts to create our processes.

Execution questions follow up on process and foundational concept questions.
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#DataScience #MachineLearning #jobinterview

These are high level scenarios like, “How do you continue researching a coding problem if there is no solution on Stack?” Competent Data Scientists have solved complex problems that aren’t on the usual research sites.
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#DataScience #MachineLearning #jobinterview

I call these execution questions because they require the candidate to have real world experience, or their answers are generic and high level.
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#DataScience #MachineLearning #jobinterview

Candidates can memorize their way through a coding interview or brute force their way through something like HackerRank. It is much harder to fake practical applications of foundational concepts and building repeatable processes.
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#DataScience #MachineLearning #jobinterview