A significant source of noise in the hiring process comes from companies being inconsistent in their interview questions, leading to a gut call that can misrepresent candidates' abilities.

When interviewing candidates, we assess their problem-solving skills and how they think about trade-offs, rather than just their ability to write code.

A candidate's motivation and depth of thinking can be assessed during interviews, which is crucial for hiring decisions.

When pre-screening candidates, we evaluate programming productivity, academic computer science skills, debugging abilities, and system design capabilities.

Successful data scientists are those who question their assumptions and seek to understand the foundations of the data they work with.

We hire data scientists who possess strong quantitative backgrounds and can approach problems from first principles, rather than just relying on tools.

A common mistake among applicants is assuming the data is perfect, which can lead to distorted findings if they don't anticipate potential issues.

The quality of a candidate's thinking should be evaluated through the questions they ask during the interview process.

Over time, we learned that certain questions we asked were indicators of whether a cleaning professional would be a good or bad performer on the platform. This insight came from data collection and analyzing responses.