Credit Risk Modeler
Global Financial Services Client now require a Credit Risk Modeler to join a high-performing Product Analytics & Innovation team building market-leading credit risk scores, predictive models, and AI-driven commercial data products on modern cloud infrastructure.
Core Objective : Independently build a point-of-application risk scorecard from scratch within a tight window. The ideal candidate must take raw, uncleaned data and deliver a fully validated, production-ready logistic regression model.
Hands-on Scorecard Build History: Must have personally built and deployed at least 2 end-to-end credit scorecards (consumer or commercial). We are looking for someone who writes the code and build the bins rather than a manager or analyst who only reviews the output.
Hands-On Python Execution: Advanced, fluent Python coder .
Must be comfortable writing custom data transformation functions and debugging logic live without relying on template scripts or AI coders.
Pragmatic Data Engineering: Strong SQL skills to ingest, merge, and clean messy, high-dimensional datasets independently in cloud environments (GCP/BigQuery preferred).
The Data Scientist will:
- Independently prepare complex datasets, run exploratory analysis, and build predictive models, risk scorecards, and decisioning tools
- Drive commercial product innovation — identify market gaps, prototype algorithms, take concepts to market-ready products
- Design and optimise data pipelines integrating large volumes of disparate commercial data
- Translate data assets into actionable business strategy and long-term analytics roadmap
- Apply advanced statistical/ML methods to uncover patterns in high-dimensional data
- Solve cross-domain problems (commercial risk, business failure, fraud detection) with engineering, product, and strategy teams
- Communicate complex findings clearly to technical and non-technical stakeholders
- Maintain data quality, governance, validation, and regulatory compliance standards
- Stay current with cloud capabilities (primarily GCP) and modern analytical tooling
- Mentor junior data scientists and lead code/quality reviews
The ideal Data Scientist will have the following expeirence:
- STEM degree (Master's preferred)
- Proven experience working in a Data Scientist or quantitative modeling role
- Extensive experience with commercial data assets (e.g. business registry, trade credit, or bureau data)
- Strong commercial data interpretation, auditing, and validation skills
- Python and SQL (Unix/shell scripting a plus)
- Foundational credit risk modeling / scorecard lifecycle knowledge (sampling, WoE, scaling)
- Git and CI/CD workflow experience
- Hands-on cloud development experience (GCP preferred)
- Exposure to ML methods (XGBoost, Random Forests, Neural Networks) and traditional stats (Logistic Regression)
- Awareness of data security, governance, and model risk management standards
Nice to Have
- UK commercial lending/regulatory knowledge (PRA/FCA, Consumer Duty, Basel 3.1)
- Experience building/validating commercial credit scorecards
- Exposure to Open Banking, transactional data, bureau feeds, ESG data
- Track record of independently pitching and delivering analytical products
Rate: £700p/d Inside IR35
Location: London (hybrid)
Duration: 6 months rolling