Lead Data Scientist: End-to-End ML for Regulated Finance

My client is a fast-growing UK business serving thousands of customers. They are investing heavily in their data capability and are now looking to appoint a Lead Data Scientist to drive end-to-end machine learning delivery within a regulated financial environment.

This is a hands-on role combining technical ownership and production-grade model deployment.

The Role

As Senior Data Scientist, you will:

• Own end-to-end ML solutions – from problem framing and feature engineering to deployment, monitoring, and governance
• Translate business objectives into modelling strategies aligned to risk appetite and operational constraints
• Build and deploy models using Python, SQL, and AWS (SageMaker or equivalent)
• Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions
• Establish monitoring frameworks for performance, drift, and retraining
• Drive clear documentation, traceability, and governance appropriate for a regulated environment

This role requires someone who thinks beyond experimentation – focusing on operational impact, adoption, and long-term model performance.

• Proven commercial ML/Data Science delivery with measurable impact
• Experience taking models into production and managing performance over time
• Prior experience leading or mentoring Data Scientists
• Strong Python (pandas, numpy, scikit-learn or similar)
• Strong SQL (complex joins, aggregations, analytical functions)
• Experience working closely with Engineering/Data teams in production-first environments
• Comfortable operating within regulated industries
• AWS experience (S3, Athena/Glue, IAM, Lambda)
• SageMaker or equivalent ML platform experience
• Financial services domain knowledge (risk, fraud, affordability, payments)
• Experience with model explainability and governance documentation
• Additional paid leave (birthday, charity, wellbeing, life events)
• Employee assistance programme & Virtual GP

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