Interviewed at Block for a mid-senior DS role on the Cash App Trust and Safety team in February 2026. Did not get the offer (they went with someone with more fraud/risk modeling experience specifically) but the process was thorough enough that I can give you a real picture.
Interview structure for DS at Block: Recruiter screen (30 min, standard) DS phone screen (60 min, SQL + one stats question) Onsite: 4 rounds total SQL deep dive Product and metrics round Statistics and ML round Behavioral / leadership round
SQL round. Harder than most. The problems aren't 'write a GROUP BY.' Mine involved window functions, a self-join on a transactions table, and a question about detecting anomalous behavior over a rolling window. If you're not comfortable with LAG/LEAD and PARTITION BY, prep that. They use BigQuery internally, or at least the interviewer mentioned BQ syntax at one point.
Product and metrics round. This was the most interesting one. They gave me a scenario: 'Cash App introduced a new feature to help users track recurring expenses. How would you measure if it's working?' I had to define success metrics, identify potential confounders, and design an A/B test. They pushed on experiment design specifically: what's your randomization unit, how do you handle network effects in a peer-to-peer payments context, what's the minimum detectable effect size you'd accept.
Statistics and ML. Covered probability (basic conditional prob, Bayes), model evaluation metrics, and one question about class imbalance in a fraud detection context. They wanted SMOTE / resampling discussion and alternatives.
Overall the DS loop felt closer to a rigorous ML eng loop than some 'can you make a chart in Tableau' DS loop. Bring your stats and your experiment design game.