Revolut

data scientist for risk models in fintech

In short

Revolut is hiring a remote Data Scientist to build and maintain risk and margin models (VaR, Expected Shortfall, SPAN, CCP-style IM/VM, liquidation logic) for liquidity and market derivatives. The role requires 3+ years of experience in machine learning or financial engineering, strong quantitative modelling skills (GARCH, ARIMA, stochastic calculus), advanced Python and SQL, and close collaboration with Engineering to deploy models into production. It involves preparing compliance documentation and presenting risk insights to senior leadership.

About
Revolut provides financial products for spending, saving, investing, exchanging, and travelling, helping its customers manage their money with greater visibility, control, and freedom.
Responsibilities
•Develop and maintain methodologies and policies for liquidity and market derivative modelling
•Deliver data-driven solutions that create product impact
•Build, enhance, and maintain the core risk engine, including margin models, leverage frameworks, and liquidation logic
•Develop and calibrate risk models for isolated and cross margin, partial and full liquidation, bankruptcy pricing, and future portfolio margining
•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 compliance documentation 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 and liquidity risk management
•Expertise in building and validating risk and margin models, including VaR, Expected Shortfall, SPAN, CCP-style IM/VM, stress tests, and scenario simulations
•Proficiency in quantitative modelling, time-series methods (GARCH, ARIMA, stochastic calculus), advanced Python, and SQL for large-scale data analysis
•Familiarity with high-frequency datasets and fast-paced risk environments
Nice to have
•Experience at a major financial institution, asset manager, or liquidity provider, or in a treasury or risk department (FTP, ALM, liquidity, IRRB, etc.)
•Exposure to market-making workflows and risks (inventory management, P&L drivers, spread optimisation, and internalisation strategies)
•Familiarity with regulatory and institutional risk frameworks (Basel III/IV, FRTB, SA-CCR, CCP margin methodologies)
Conditions
•Financial benefits
•Revolut Metal subscription with perks
•Year-round events

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