Risk Analyst, Cleo AI

• 4+ years of experience in an analytical or risk-focused role within fintech, lending, or payments
• Strong foundations in SQL and experience working with large, complex datasets
• Proficiency in Python (or R) for data analysis and statistical testing
• Deep understanding of risk metrics and model performance concepts (e.g. AUC, Gini, calibration, PSI/CSI, drift, etc.)
• Experience with data visualisation
• Strong analytical rigour and the ability to translate findings into business implications
• Ability to define and measure success metrics for decision systems and product changes
• Direct experience with Bayesian A/B testing methodologies

Desirable

• We’re looking for a Risk Analyst to help us measure, monitor, and improve the performance of Cleo’s credit products. This is a level 3 or 4 Analytics role. Cleo has 5 internal levels of competency within Analytics. See our skills framework for how we differentiate on seniority
• You’ll sit within the Risk & Payments pillar, working at the intersection of data, decisioning, and product, helping us build scalable systems that balance user access with sustainable economics
• This is a hands-on analytics role, suited to someone with strong technical foundations and a curiosity for how data translates into better decisioning
• You’ll work closely with outer Analysts, Product Managers, Data Scientists, and Engineers to diagnose portfolio trends, build monitoring frameworks, and deliver insights that inform how Cleo manages and optimises risk
• You’ll be part of a growing team responsible for driving profitable growth while protecting the business from loss, using data to understand repayment behaviour, model performance, and system-level trade-offs
• This is an opportunity to shape how we quantify and manage risk as we expand across new credit products and geographies
• Analyse arrears, default, and yield trends across Cleo’s credit products
• Identify emerging risks and shifts in eligibility or repayment behaviour using cohort and segmentation analysis
• Build and maintain dashboards for portfolio health and performance tracking
• Design early-warning alerts for anomalies in arrears or model-driven decisioning
• Partner with our Risk Modelling team to interpret model outputs (AUC, PSI, calibration, feature drift)
• Monitor model stability and support investigations into concept drift and feature degradation
• Quantify the impact of model changes and assess whether observed shifts are model- or market-driven
• Conduct root-cause analysis on performance deteriorations (e.g., arrears spikes, yield compression)
• Use decomposition, SHAP analysis, and driver frameworks to explain variance in loss and yield
• Support the design and measurement of A/B tests or pilot changes in credit decisioning or repayment operations
• Partner with Finance and Commercial teams to support variance analysis and monthly forecast inputs
• Model how shifts in repayment or eligibility rates flow through to portfolio loss and profitability
• Work with Analytics Engineering to improve risk data pipelines and metric definitions
• Build reusable analysis templates and frameworks for monitoring across multiple credit products
• Communicate insights clearly to non-technical stakeholders, transforming complex findings into actionable decisions