Real, anonymous reports from people who interviewed for Data Scientist at Meta, newest first and distilled into what to expect across the loop.
I went through a fairly standard Meta Data Scientist loop centered on SQL and product analytics, with a smooth but metrics-heavy cadence over a few weeks.
I went through an interview path that stayed SQL- and product-case focused, with one or two probability/statistics moments and a clearly guided structure, though I still didn’t move forward.
The interview process typically begins with a recruiter screen focused on fit, background, and expectations for the role. This step is crucial for setting the tone and understanding the interview structure.
Candidates can expect a technical screening that emphasizes SQL and product analytics, often including medium-level SQL questions and product case scenarios that require defining metrics and outcomes.
The onsite loop consists of multiple rounds that assess analytical execution, product reasoning, and behavioral fit, with a focus on how candidates connect metrics to business decisions and handle collaboration challenges.
Expect questions related to statistics, particularly around A/B testing and probability, where candidates must demonstrate their ability to design experiments and interpret statistical results.
Interviews often require candidates to engage in a conversational style, mirroring the interviewer's reasoning process while articulating their thought process clearly, especially during product and analytical discussions.
Candidates report a medium to high difficulty level across the process, with varying experiences in interview consistency and clarity of expectations, impacting their performance and outcomes.