
Revolut Data Scientist interview typically runs 5 rounds: introductory call, coding challenge, technical rounds, seniority-defining interview, and culture fit. The process spans roughly 4–6 weeks and is distinguished by heavy emphasis on coding assessments alongside product and ML evaluation.
$118K
Avg. Base Comp
$150K
Avg. Total Comp
4-5
Typical Rounds
4-6 weeks
Process Length
We've seen Revolut lean hard into a very specific profile: someone who can move comfortably between algorithmic coding and applied data work across multiple evaluation stages. Multiple candidates reported that the technical bar was not limited to SQL or product sense — it also included LeetCode-style implementation, Python problem solving, and questions that tested how candidates think through data labeling or KPI measurement. That mix is a strong signal that Revolut wants data scientists who can operate as strong generalists, not just analysts with a notebook. The funnel itself moves from an introductory recruiter call through automated coding assessments, then into technical rounds, a seniority-defining interview, and a culture-fit discussion — so candidates need to sustain that breadth across several distinct conversations over four to six weeks.
A recurring theme is the emphasis on structured reasoning under pressure. Candidates described live coding where they had to explain their thought process as they worked, plus SQL questions involving many joins and open-ended analysis prompts covering product, experimentation, and business impact. More conceptual ML topics also surfaced — assumptions of linear regression, solo ML deployment — which suggests Revolut cares about whether candidates understand how models behave in real systems, not just how to fit them. The non-obvious trap is assuming the interview will stay at the level of dashboards or business metrics; our candidates report that Revolut often pushes into implementation details and model judgment without much warning.
What stands out most is the sheer breadth of evaluation across the funnel. Even when the early conversation felt conversational, the later technical work quickly became dense and multi-disciplinary. The candidates who do best here are the ones who can connect code, SQL, and product logic in one coherent answer — treating them as a unified skill set rather than separate disciplines to compartmentalize.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Revolut process.
hackerrank teste de 1h de 15 minutos com 4 perguntas em python e 2 perguntas de sql
Questions asked: python3 perguntas e sql com muitos joins, a entrevista de habilidade em análise de dados é mais uma mistura de programação e perguntas abertas, abrangendo técnicas de processamento de dados, estruturas de pensamento analítico, produto e experimentação, habilidades em sql e python.
Prep tip from this candidate
Practice writing Python data processing solutions under timed pressure (4 questions in ~15 minutes each), and focus SQL prep on complex multi-table JOIN queries. Also prepare for open-ended analytical and product/experimentation questions alongside the coding, as the interview blends both technical and conceptual thinking.
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Sourced from candidate reports and verified by our team.
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 | |
| Button AB Test | |
| Daily Logins | |
| Top 3 Users | |
| Third Purchase | |
| Rolling Average Steps | |
| Total Spent on Products | |
| Find the First Non-Repeating Character in a String | |
| Post Composer Drop | |
| Cumulative Reset | |
| Size of Joins | |
| Daily Retention Summary | |
| 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 | |
| Duplicate Rows |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with a conversational call with an HR recruiter covering your background, past projects, salary expectations, and general fit. This stage is straightforward and sets expectations for the technical rounds ahead.
Candidates complete a timed HackerRank-style automated assessment featuring a mix of Python and SQL questions. SQL problems involve complex joins and KPI-style queries, while Python tasks test practical algorithmic problem-solving under time pressure.
A live coding interview conducted in a shared environment where you implement solutions to LeetCode-style problems, such as longest common prefix, while explaining your thought process. Interviewers assess both correctness and communication of your reasoning.
A deeper technical round covering SQL, Python, data labeling, KPI measurement, and analytical thinking. Expect questions spanning data processing techniques, product and experimentation frameworks, and ML or business impact topics relevant to Revolut's risk and product domains.
A final discussion assessing alignment with Revolut's values and ways of working, including how you approach ambiguous problems, collaborate cross-functionally, and demonstrate ownership. This stage may also touch on seniority-defining behavioral questions.