AI News, How Quora’s Head of Data Science Conducts Candidate Interviews

How Quora’s Head of Data Science Conducts Candidate Interviews

Eric Mayefsky, head of data science at Quora, has assessed hundreds of job candidates in his half decade in management at various tech companies.

What he’s learned from his experiences on both sides of the table can help other data science leaders navigate the chasm of assumptions between interviewee and interviewer, and make more effective hires.

His beliefs on data science interviews are defined by five lessons: In 2010, Eric was four years into an economics PhD at Stanford University, studying the performance of algorithms in matching markets.

The idea of joining the private sector involved a big shift in direction, but after years in academia, Eric felt stifled and dissatisfied by the speed of academia, and the impact of his work.

“The numbers were super awkward, and I didn’t expect him to ask me to do the math by hand,” says Eric, “If he wanted me to do the math, I’d have expected him to make the numbers easier, just to move it along.” The question made Eric think of the GRE, which adjusts questions based on the test taker’s performance.

There are three types of candidates Eric tends to encounter during the hiring process for data science roles: grad students, wide-eyed undergraduates, and people with some experience in the private sector.

“Candidates who can show that they’re very comfortable working on independent projects, that’s a big plus.” The questions you ask in an interview should help illuminate past experiences that have shaped a candidate’s thinking.

Quora sends candidates a “what to expect” memo that outlines the types of problems candidates will encounter and materials they’ll have on hand for solving those problems.

That means carving out time for long-term research projects while building tools to help the team work more efficiently, and helping to drive product decisions.

But it’s a tradeoff I’m happy to make, versus not painting an accurate picture for the candidate.” The best candidates are looking for jobs that will challenge them, so give them a challenging interview.

A couple examples of retired questions of this type include: When the candidate starts responding, he tries to stick with them to the end of their natural train of thought, and ask probing questions about areas where they’ve shown some comfort.

“If their answer didn’t make a ton of sense to me, I would try and ask questions around the pros and cons of their suggestion to understand their thought process and potentially see if they could come to any of the shortcomings of their method on their own.” He’s careful not to force the candidate down a path totally different from the one they’ve chosen in answering the question.

Quora has more than 200 million monthly active users, who interact through questions ranging from, “Why do bees die after they sting?” to “What are the best tips for raising venture capital funding?” That data is interesting!

It sounds like you’re still thinking about writing papers.” Quora won’t benefit from hiring a data scientist who is stuck in a pure research mindset to the detriment of discovering insights that can be applied to the company’s product and growth.

At a corporation with tens of thousands or hundreds of thousands of employees, employees may not be able to see their impact, and so in some cases it’s okay for them to be hyper-focused without feeling especially responsible for the company’s overall success.

The best candidates are those who demonstrate interest in finding creative ways to increase user engagement, who want to tinker with a/b testing, or want to improve ad targeting in order to better target potential users or customers with the right content and products.

Whereas some PhDs can be red-flag candidates due to a single-minded interest in the dataset, there’s another subset of PhDs that Eric says tend to excel in private sector data science jobs.

He calls this type the “disgruntled grad student.” These are graduate students who are sick of how slowly academic research moves, and want something faster and higher energy, where they can see the impact of their work sooner.

Just like the candidates who know what they don’t like, but aren’t sure of what they want, you may not know what the ideal candidate looks like if you don’t give yourself a chance to see all your candidates clearly.

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