Robinhood Data Scientist interviews, per candidate reports, combine SQL, Python, product sense and A/B testing. Reports range from a short screen-only process to a multi-round on-site with an experimentation round.
$190K
Avg. Base Comp
$260K
Avg. Total Comp
2-6 rounds
Typical Rounds
Not reported
Process Length
Candidates describe Robinhood's Data Scientist interview as a mix of technical fundamentals and product judgment, and the reports differ by stage. Two Senior Data Scientist candidates describe a recruiter screen, a combined technical and product round, and a four-round on-site: Product Analytics, Product Jam, SQL plus Python, and Experimentation. Both say the hypothetical Robinhood product case was judged on ROI and A/B testing, so tie each recommendation to measurable business impact and say how you would validate it.
SQL appears in every report. One candidate faced simple joins, another a question about user trading volume, and a third worked through window functions on two Robinhood tables in a CodeSignal section. Python in that same report meant pandas groupby and aggregation on a list of trades. Practice trade-style data and talk through partitioning, ordering and aggregation as you write.
Experiment design is the other recurring theme. A Data Scientist candidate who later accepted an offer reported an A/B test design question in the first technical round. Be ready to cover hypothesis, metrics, tradeoffs and how you would read results. One candidate who did not get an offer found the statistics question hardest, so rehearse explaining your reasoning out loud rather than reciting formulas. Outcomes vary across the reports, and the sample is small.
Synthesized from 4 candidate reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Robinhood
Write a SQL query that creates a cumulative distribution of the number of comments per user with bin buckets of one
| Question | |
|---|---|
| Experiment Validity | |
| Like Tracker | |
| Bank Fraud Model | |
| Fractional Shares | |
| Google Maps Improvement | |
| Completed Shipments | |
| Same Side Probability | |
| Coefficients of Logistic Regression | |
| Variable Error | |
| Level Of Rain Water In 2D Terrain | |
| ATM Robbery | |
| Mutated Offspring | |
| Marketing Channel Metrics | |
| Hurdles In Data Projects | |
| Portfolio Platform Architecture | |
| Identifying Good Investors | |
| Nightly Job | |
| Coin Flip Probability | |
| A/B Testing a Checkout Button Change | |
| Free Seats | |
| NxN Grid Traversal | |
| Index Fund Return | |
| Market Opening Experiment | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Employee Salaries |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a conversational recruiter call with baseline gatekeeping questions about background and fit. Two candidates also say they were asked to describe a project they were proud of, so prepare a concise story where data drove a decision.
Two Senior Data Scientist candidates describe a combined round with a SQL question on joins, a behavioral prompt about using data to drive a decision, and a hypothetical Robinhood product case. Interviewers reportedly wanted answers framed around ROI and validated through A/B testing.
One candidate reports a hiring manager screen mixing background questions with CodeSignal SQL and Python. The SQL used window functions across two Robinhood tables, the Python used pandas on a list of trades, and the discussion added product sense and statistics questions. That candidate did not receive an offer.
A Data Scientist candidate, referred by a former colleague, describes an initial technical round built around a SQL question on user trading volume and an open-ended A/B test design question. They report receiving an offer a few days later, which they accepted.
Two Senior Data Scientist candidates report four on-site rounds: Product Analytics, Product Jam, SQL plus Python, and Experimentation. Product Jam was described as collaborative and brainstorming-oriented, and the SQL and Python round as deeper than the earlier screen.
