Revolut Data Scientist candidates report recruiter screening, SQL and statistics assessments, live coding, and product or case-focused discussions. Preparation should combine clear reasoning with hands-on SQL, Python, experimentation, and project storytelling.
$150K
Avg. Base Comp
$183K
Avg. Total Comp
3-4 rounds
Typical Rounds
4-6 weeks
Process Length
Revolut Data Scientist candidates commonly describe an interview path that tests more than dashboarding or routine analysis. SQL reasoning is a recurring centerpiece: reports include live queries against small transaction-style datasets, joins and window functions, KPI measurement, and explaining assumptions, edge cases, or alternate approaches while coding. Candidates also reported Python data-labeling work and LeetCode-style live coding.
Early assessments may combine SQL with statistics and A/B testing. Candidates specifically reported probability questions, Bayes’ theorem, statistical significance, binomial-distribution reasoning, sample-size estimation, and machine-learning evaluation concepts. Technical conversations can broaden into model design or deployment, and one report described an NLP-oriented session focused on system design.
Prepare project stories with the same precision. Candidates report questions about their prior projects, individual contribution, technical choices, and specific details of their work. Case-style discussions have included diagnosing a metric decline and explaining business tradeoffs. Practice stating a sensible assumption, choosing a first query or analysis, and then making the conclusion understandable to a product audience.
Reports vary by team and specialization, so the exact sequence and emphasis are not consistent.
Synthesized from 13 candidate reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Revolut
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Experiment Validity | |
| Last Transaction | |
| Like Tracker | |
| Month Over Month | |
| Daily Logins | |
| Button AB Test | |
| Top 3 Users | |
| Third Purchase | |
| Rolling Average Steps | |
| Retailer Data Warehouse | |
| Total Spent on Products | |
| Find the First Non-Repeating Character in a String | |
| Daily Retention Summary | |
| Post Composer Drop | |
| Cumulative Reset | |
| Size of Joins | |
| Google Maps Improvement | |
| Declining Applicants | |
| Payments Received | |
| Subscription Retention | |
| Time on FB Distribution | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Word Frequency | |
| Assumptions of Linear Regression | |
| Testing Price Increase |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial conversation about prior work, projects, technical stack, motivations, salary expectations, and sometimes location or relocation. Be ready to explain your own contribution and how you have used Python, SQL, or models in practice.
Several candidates report a SQL-heavy assessment or HackerRank-style test. Reported topics include joins, window functions, KPI questions, probability, A/B testing, Bayes’ theorem, statistical significance, and machine-learning basics; the precise mix may vary.
Candidates report live work in SQL, algorithmic coding, or Pandas against sample data. Examples include transaction analysis, data labeling, and explaining query choices. Interviewers may focus on assumptions, edge cases, and tradeoffs as well as the working solution.
Candidates report later discussions that can cover product or business cases, project deep dives, machine-learning fundamentals, model design, deployment, or system design. Practice linking a problem, your approach, and the metric or decision it informed.
