Senior / Lead Data Scientist, Credit Risk Analytics in London

We’re looking for a Lead / Senior Data Scientist to help us measure, monitor, and improve the performance of Cleo’s credit products. This is a hands‑on data science and analytics role. You’ll be analysing behaviour across millions of US users, using rich transactional and behavioural data that powers Cleo’s AI money coach and credit products. You’ll spend the majority of your time in SQL and Python, working directly from Cleo’s data warehouse to understand, explain, and improve credit performance. You’ll be the analytics owner for [EWA / specific product], with direct line of sight to losses, revenue, and product roadmap. You’ll work closely with other analysts, Risk Modellers, Product Managers, and Engineers to diagnose portfolio trends, build monitoring frameworks, and deliver insights that inform how Cleo manages and optimises risk. 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. 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.

What You’ll Be Doing

• Credit & Risk Performance Analytics
• Write complex SQL/Python to pull cohort‑ and event‑level datasets from our warehouse and turn them into clear, decision‑ready analyses.
• Quantify the commercial impact of performance changes (losses, yield, approval rate).
• Design and analyse multivariate experiments on underwriting, pricing, or repayment flows, and translate results into actionable risk strategies.
• 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.
• Model Understanding & Monitoring
• Partner with the Risk Modelling team to turn model health metrics (AUC, PSI, calibration, feature drift) into clear recommendations for policy or product changes.
• 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.
• Deep‑Dive Investigations
• Conduct root‑cause analysis on performance deteriorations (e.g., arrears spikes, yield compression).
• Own investigations from question → analysis → recommendation, and present your work to Risk, Product, and Leadership.
• 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.
• Forecasting & Scenario Support
• 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.
• Tooling, Frameworks & Collaboration
• 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.

About You

• 4+ years analytics or data science experience in a risk‑focused role, ideally within fintech, lending, or payments.
• Excellent SQL skills.
• Fluency in Python (or R) for data analysis, modelling, and statistical testing.
• 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.
• Hands‑on experience with predictive models (e.g. credit, fraud, marketing), including interpreting metrics such as AUC/Gini, calibration, PSI/CSI, drift.
• Hands‑on experience with BI tools (e.g. Looker, Mode, Tableau) and data workflow tools (dbt, Airflow).
• Strong analytical rigour and the ability to translate findings into clear business recommendations.
• Track record of taking analyses all the way through to shipped changes and measurable impact.

Nice to Have

• Exposure to credit risk or payments decisioning (eligibility, pricing, loss modelling, or fraud detection).
• Experience with model monitoring, feature engineering, or supporting ML deployment.
• Familiarity with US and/or UK consumer credit or payments regulations.

Benefits

• A competitive compensation package (base + equity) with 3‑annual reviews, aligned to our termly OKR planning cycles.
• AX3 banding: £74,266 - £96,126 (Hybrid London or £69,699 - £90,657 UK Remote).
• AX4 banding: £94,059 - £119,128 (Hybrid London or £88,938 - £113,489 UK Remote).
• Work at a fast‑growing tech startup backed by top VC firms (Balderton & EQT Ventures).
• A clear progression plan with opportunities to lead, challenge the status quo, and own impact.
• Flexibility to work where you perform best, with support for hybrid or remote arrangements.
• Coverage of travel costs for occasional in‑office or remote meetings.

Other Benefits (geographical location may vary)

• Company‑wide performance reviews every 4 months.
• Generous pay increases for high‑performing team members.
• Equity top‑ups for team members getting promoted.
• 25 days annual leave + public holidays (+ an additional day for every year you spend at Cleo, up to 30 days).
• 6% employer‑matched pension in the UK.
• Private Medical Insurance via Vitality, dental cover, and life assurance.
• Enhanced parental leave.
• 1 month paid sabbatical after 4 years at Cleo.
• Regular socials and activities, online and in‑person.
• Company subsidy for OpenAI subscription.
• Online mental health support via Spill.
• Workplace Nursery Scheme.

Equal Opportunity Statement

We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio‑economic backgrounds. If there’s anything we can do to accommodate your specific situation, please let us know.