Jasmine Tsai got her first taste of data engineering through a common gateway: she was tasked with a data project as a software engineer. Tsai was part of a team that rewrote Change.org’s non-profit subscriptions system from scratch, to turn it into a stream processing system. This project introduced her to working with data problems.

When Jake Klamka founded Insight Data Science in 2011, the term “data science” had a much different understanding in the private sector than it does today.

Eric Mayefsky, head of data science at Quora, has assessed hundreds of job candidates in his half decade in management at various tech companies. But like any manager, he started on the other side of the interview: as an applicant.

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.

Hiring a data scientist can be a tricky process. The actual definition of “Data Scientist” is vague, the day-to-day job of someone with “Data Scientist” in their job title varies dramatically between organizations, and people come to the field from a wide variety of backgrounds. Examining the past of a data scientist candidate is a science in itself, one worthy of a blog post of its own. Today we're going to stick to building an interview that examines the present.