Data Scientist (Risk)

About Revolut

People deserve more from their money. More visibility, more control, and more freedom. Since 2015, Revolut has been on a mission to deliver just that. Our powerhouse of products — including spending, saving, investing, exchanging, travelling, and more — help our 75+ million customers get more from their money every day.

As we continue our lightning-fast growth, 2 things are essential to our success: our people and our culture. In recognition of our outstanding employee experience, we've been certified as a Great Place to Work™. We have 13,000+ people working around the world, from our offices and remotely, to help us achieve our mission. We're looking for more brilliant people who love building great products, redefining success, and turning the complexity of a chaotic world into the simplicity of a beautiful solution.

About The Role

Our Data Science team solves complex problems with smart, practical solutions. Data Scientists and Analysts work directly with product teams to uncover insights, guide decisions, and improve how customers experience Revolut. They do this by delivering smart, scalable solutions that move the business forward.

We're looking for a Data Scientist who'll effectively assess and manage risk by developing policies, methodologies, models, and systems for risk quantification, reporting, and monitoring.

What You'll Be Doing

• Developing and maintaining methodologies and policies for liquidity and market derivative modelling
• Delivering real impact to the product through rigorous data-driven solutions
• Building, enhancing, and maintaining the core risk engine, including margin models, leverage frameworks, and liquidation logic
• Developing and calibrating risk models across isolated/cross margin, partial/full liquidation, bankruptcy pricing, and future portfolio margining capabilities
• Collaborating with Engineering to deploy risk methodologies into production systems while iteratively improving models based on market conditions
• Automating advanced analytic workflows, including P&L attribution, daily risk reporting, short-term investment performance, and cash flow analysis
• Preparing documentation for compliance, and presenting risk insights, exposures, and recommendations to senior leadership

Requirements

• A 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 the 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, and internalisation strategies)
• Familiarity with regulatory and institutional risk frameworks (Basel III/IV, FRTB, SA-CCR, CCP margin methodologies)