Went through the Perplexity DS loop in February 2026. Senior DS role. Sharing this because I couldn't find anything specific about their DS process before interviewing.
Recruiter screen: Standard. They asked about my stack (SQL, Python, experiment design) and why I wanted to do DS at an AI search company. I mentioned I was interested in the challenge of measuring quality in generative outputs, which seemed to resonate.
Technical screen (45 min with a DS on the team): Two SQL problems. The first was a window function / ranking question (find the Nth highest value per group, classic). Second was messier: multi-table join with some ambiguous logic that required me to ask clarifying questions about how the tables related. The second problem was clearly more important. They watched how I handled ambiguity.
After SQL, we spent 10 minutes on stats. The question was: you're running an A/B test on answer quality. Users in variant B rate answers higher. Walk me through how you'd decide if this is real. I talked about sample size, test duration, novelty effect controls, checking for covariate imbalance. They asked specifically about what I'd do if click-through rate and explicit rating moved in opposite directions. Good question. I said I'd treat them as separate hypotheses measuring different user behaviors and wouldn't combine them.
Case study (60 min with a PM and senior DS): Given a scenario: Perplexity launches a new feature that shows related questions at the bottom of a search result. How do you measure if it's working? No data provided, open-ended. I scoped the goal (engagement vs. satisfaction vs. long-term retention), proposed metrics, designed a rough experiment, called out the things that could make the data misleading (related questions might cannibalize the original query's value rather than add to it).
They pushed on: how do you know if a user clicking a related question is a success signal or a "they didn't find their answer" signal. That's the real complexity. I talked about session depth, return rate, and qualitative signal from follow-up queries.
What made the difference (at least I think): Treating the ambiguity as a feature, not a bug. DS at an early-ish company means the data is messy and the product questions are novel. They seemed to want someone comfortable with that, not someone who needs a clean dataset handed to them.