Data Science Manager, Cleo AI

• Strong analytical skills with fluency in SQL and Python
• Ability to interpret and communicate model performance metrics, feature importance, and confidence intervals
• Background in collaborating with decision science or data science teams on feature engineering and model evaluation
• Experience conducting large scale A/B experiments and interpreting results to drive product and business decisions
• Fluent in credit portfolio metrics – e.g. arrears buckets, roll rates, loss rate, yield/marginal loss – and how they tie to unit economics and P&L
• Experience building and maintaining performance monitoring systems and alerting frameworks
• Hands‑on experience working with predictive models (e.g. credit, fraud, marketing), including interpreting metrics like AUC/Gini, calibration, PSI/CSI, drift
• Experience collaborating with commercial and finance teams on business metric forecasting
• Track record of taking analyses all the way through to shipped changes and measurable impact
• Experience managing or mentoring analysts and structuring team output for maximum impact
• Skilled at translating technical analysis into actionable recommendations for senior stakeholders
• Able to balance short-term monitoring needs with long-term framework building

Desirable

• We’re looking for a Data Science Manager to lead the measurement, monitoring, and optimisation of Cleo’s credit performance. This is a high-impact role at the heart of how we manage risk and drive sustainable growth. You’ll own the frameworks, metrics, and deep-dive analyses that keep our credit products healthy and ensure that when performance shifts, we understand why and act quickly
• Sitting within the Risk & Payments pillar, you’ll lead a team of analysts to deliver clear, actionable insights across arrears, yield, LTV, and portfolio performance, working closely with Credit Policy, Decision Science, Product, and Finance. You’ll be setting the measurement standards for the company, diagnosing portfolio trends, and shaping how we evaluate and implement policy changes
• This role is ideal for someone who thrives at the intersection of data, systems thinking, and stakeholder influence. You’ll be equally comfortable explaining SHAP outputs to a data scientist, talking loss economics with finance, and summarising driver analysis for senior leadership
• Own the design and maintenance of Cleo’s credit risk metric framework, including arrears, default, yield, LTV, and marginal loss rates
• Build and maintain dashboards and alerts for early detection of arrears, roll-rate shifts, and decisioning anomalies
• Ensure definitions, thresholds, and escalation processes are consistent, documented, and used company-wide
• Partner with the Risk Modelling team to turn model health metrics (AUC, PSI, calibration, feature drift) into clear recommendations for policy or product changes
• Detect and diagnose feature shift or concept drift, ensuring model inputs remain valid and predictive
• Lead investigations into performance deviations, separating model-driven changes from macro or operational causes
• Lead root cause analysis for deterioration in key metrics such as arrears spikes or yield compression
• Own investigations from question → analysis → recommendation, and present your work to Risk, Product, and Leadership
• Deliver driver analysis (SHAP, feature importance, decomposition) for changes in portfolio performance
• Quantify the risk-adjusted impact of new or changing product features on portfolio health
• Design and analyse multivariate experiments on underwriting, pricing, or repayment flows, and translate results into actionable risk strategies
• Partner with Credit Policy and Decision Science to design robust evaluation frameworks for policy changes
• Quantify the marginal impact of policy shifts, controlling for macro or seasonal effects
• Support elasticity and profitability modelling to optimise amounts, pricing, and feature-level decisioning
• Manage and mentor Credit Data Scientists/Analysts, setting clear priorities and deliverables
• Translate complex analytical findings into clear business implications for senior leadership
• Build strong cross-functional relationships with Commercial, Product, Data Science, and Finance