Meta Data Scientist Interview Guide

My Meta DS interview process consisted of two 45-minute rounds: behavioral and analytical reasoning. Having worked at Meta before, I was familiar with the environment, but that didn’t make the interviews stress-free. I felt most comfortable discussing a recent project, handling disagreements, and explaining how I scoped ambiguous problems. The question that caught me off guard was how I’d make a forecast using historical data that was biased or unreliable—and communicate the uncertainty around it. I mentioned RMSE, prediction intervals, and validating against a period with cleaner data, but afterward I felt I hadn’t connected those ideas into a sufficiently concrete approach. To make things more stressful, I lost the interviewer’s video on Zoom during that question. I kept going because I didn’t want to interrupt the conversation, and we reconnected during the questions at the end, but it definitely added anxiety. My honest takeaway: discussing work you know inside out feels very different from constructing a rigorous answer to an unfamiliar problem while someone is watching. That forecasting question was the one I kept replaying afterward.

Questions asked: The questions I remember from the behavioral round were: walk me through a recent project; describe how you handled a disagreement, including situations you handled well and situations you could have handled better; and explain how you scope an ambiguous problem. There was also a more technical question about forecasting when the historical data is biased or inaccurate: how would you build the forecast and communicate uncertainty? A clarification was that we could assume we had identified the root cause and could collect accurate data going forward, even though the historical data remained unreliable. That detail made the question more challenging—it required thinking through how to use the flawed history alongside a shorter period of trustworthy data, rather than simply saying we would fix the data first.

The analytical reasoning round focused on a messaging platform testing a new service for businesses to communicate with customers. I was asked how I would define success, design an evaluation to determine whether the pilot was working, and choose businesses for a broader rollout. The final part involved taking a positive experimental result and deciding what data and visuals would help a partnerships team explain the benefits to potential customers. The case covered both analytical rigor and business considerations.