Revolut Data Scientist Interview Guide

We’ve seen Revolut lean hard into a very specific profile: someone who can move comfortably between algorithmic coding and applied data work. 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 like strong generalists, not just analysts with a notebook.

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 with many joins and open-ended analysis prompts about product and experimentation. We also saw more conceptual ML topics surface, like assumptions of linear regression and solo ML deployment, which suggests the company cares about whether candidates understand how models behave in real systems, not just how to fit them. The non-obvious trap here is assuming the interview will stay at the level of dashboards or business metrics; our candidates report that Revolut often pushes beyond that into implementation details and model judgment.

What stands out most is the breadth of evaluation. Even when the early conversation felt conversational, the later technical work quickly became dense and multi-disciplinary. In our view, the candidates who do best here are the ones who can connect code, SQL, and product logic in one coherent answer, rather than treating them as separate skills.