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Revolut is a global fintech company on a mission to give people more from their money through products spanning spending, saving, investing, exchanging, and travel. With 80+ million customers and 13,000+ employees worldwide, the company is scaling rapidly.
The Data Science team solves complex problems with smart, practical solutions, working directly with product teams to uncover insights and guide decisions. This role focuses on risk management—developing policies, methodologies, models, and systems for risk quantification, reporting, and monitoring.
Key Responsibilities:
- Develop and maintain methodologies and policies for liquidity and market derivative modelling
- Deliver data-driven solutions that create real impact on the product
- Build, enhance, and maintain the core risk engine, including margin models, leverage frameworks, and liquidation logic
- Develop and calibrate risk models across isolated/cross margin, partial/full liquidation, bankruptcy pricing, and future portfolio margining capabilities
- Collaborate with Engineering to deploy risk methodologies into production systems and iteratively improve models based on market conditions
- Automate advanced analytic workflows, including P&L attribution, daily risk reporting, short-term investment performance, and cash flow analysis
- Prepare documentation for compliance and present risk insights, exposures, and recommendations to senior leadership
Requirements:
- Degree in mathematics, statistics, financial engineering, machine learning, or computer science
- 3+ years of experience in machine learning, financial engineering, or similar roles within liquidity or market derivatives
- Knowledge of margin trading mechanics, derivatives pricing models, and liquidation/liquidity risk management
- Expertise in building and validating risk and margin models (VaR, Expected Shortfall, SPAN, CCP-style IM/VM, stress tests, scenario simulations)
- Proficiency in quantitative modelling and time-series methods (GARCH, ARIMA, stochastic calculus), with advanced Python and SQL skills for large-scale data analysis
- Familiarity with high-frequency datasets and ability to work well in fast-paced risk environments
Nice to Have:
- Experience at a major financial institution, asset manager, or liquidity provider, or within a treasury or risk department (FTP, ALM, liquidity, IRRB, etc.)
- Exposure to market-making workflows and risks (inventory management, P&L drivers, spread optimisation, internalisation strategies)
- Familiarity with regulatory and institutional risk frameworks (Basel III/IV, FRTB, SA-CCR, CCP margin methodologies)
