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# Head of Data Science, Credit Risk Analytics

- **Company:** Cleo
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Python (Programming Language), Standard Sql, Model-Driven Development
- **Published:** September 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=bece0cc5c9187549

## About the Role

* 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

## Description

We're hiring a Data Science Manager 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.