Head of Data Science, Credit Risk Analytics

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Here at Cleo we have team of ~135 member in our Borrow Pillar and ~8 dedicated to Credit Policy and Analytics. This function brings together several specialisms, all working toward managing portfolio performance; designing the systems, models, and controls that make lending sustainable. They drive to build and grow a portfolio of responsible EWA & Pay Later products that can scale across markets while protecting users from high cost debt trap.

We're hiring a Head of Data Science to lead credit performance measurement and monitoring within our Risk & Payments pillar
. This is a player-coach role:
You'll manage a team of Credit Data Scientists and Analysts, splitting your time between hands-on analysis and leading the team - setting direction, developing people, and owning the function's output.

You'll own the frameworks and metrics that keep our credit products healthy, and drive the deep-dive analyses that explain performance shifts and turn them into action. Key areas of impact:

• Own credit performance measurement - design and maintain Cleo's risk metric framework (arrears, default, yield, LTV, marginal loss), and the dashboards/alerts that catch arrears spikes, roll-rate shifts, and decisioning anomalies early.

• Bridge model health and business decisions - partner with Risk Modelling to translate model metrics (AUC, PSI, calibration, drift) into policy and product recommendations, and diagnose whether performance moves are model-driven, macro, or operational.

• Lead root-cause analysis - own investigations end-to-end (question → analysis → recommendation) on issues like arrears spikes or yield compression, using driver analysis (SHAP, feature importance, decomposition), and present findings to Risk, Product, and Leadership.

• Support policy and decisioning - work with Credit Policy and Decision Science to build evaluation frameworks for policy changes, quantify their marginal impact, and support elasticity/profitability modelling on pricing and limits.

• Manage and develop a team - directly manage Credit Data Scientists and Analysts: set priorities, manage performance, and grow the team, not just direct project work.

• Strong SQL and Python skills, comfortable working directly from the warehouse

• Track record owning analyses end-to-end - from framing the question to shipping a recommended change

• Hands-on experience with predictive models (credit, fraud, or similar), fluent in AUC/Gini, calibration, PSI/CSI, and drift

• Fluency in credit portfolio metrics (arrears buckets, roll rates, loss rate, yield/marginal loss) and how they tie to unit economics

• Experience directly managing analysts or data scientists - priorities, performance, and people development

• Nice to have: experience with short-term or revolving credit products

• Nice to have: experience with both UK and US regulatory frameworks

We offer benefits package built to support you in and out of work. Benefits vary by country and include meaningful equity, comprehensive health, dental…