Did the Square DS interview process a couple months ago for a mid-senior DS role on their risk team. Here's the actual breakdown.
OA: Two parts. SQL problems (3 of them, timed, real SQL not pseudocode). The queries weren't trivial: window functions, a self-join, one problem with CTEs. If you're rusty on window functions practice those specifically. The second part was probability/stats questions -- multiple choice and short answer. Topics: conditional probability, expected value, hypothesis testing setup (null/alternative, p-value interpretation), confidence intervals. Nothing advanced stats, but it was faster-paced than I expected.
Phone screen: Stat concept + case. The stat concept question was about A/B testing -- specifically, when should you stop an experiment early and what are the risks? Good answers cover peeking problems, alpha inflation, sequential testing as an alternative. The case was open-ended: "Square launches a new seller feature, describe how you'd measure its success." I went through instrumentation, North Star metric, guardrail metrics, segmentation. About 45 minutes total.
Onsite (4 rounds): SQL deep dive. More complex than the OA. One problem was about joining transaction and refund tables to find merchants with unusually high refund rates. Real-world-ish. ML/modeling round. They asked about a fraud detection use case. Walk me through how you'd build a model: data sources, features, modeling approach, how do you handle class imbalance, how do you evaluate it. I talked through precision/recall tradeoffs for fraud (you care more about recall, false negatives are worse than false positives in most fraud contexts). Case study. Given a hypothetical dataset summary, what's going wrong with our payment completion rate? Pure business problem, data-driven reasoning. Behavioral. Standard STAR. "Tell me about a time your analysis changed a business decision."
SQL and stats are both tested multiple times. Don't neglect either. My advice: practice SQL in HackerRank or Mode, and make sure you can talk through experiment design out loud without needing to write anything down.