We are seeking an experienced Scoring Engineer/Data Scientist to support a large-scale AI and fraud analytics programme within a regulated insurance environment. This is a hands-on role focused on building, deploying, and maintaining machine learning models and pipelines on Azure. Role Overview Design, build, unit test, and maintain ML models and ML pipelines on the Azure platform Support and enhance a fraud analytics platform (Quantexa experience preferred) Work closely with solution architects, data engineers, and cross-functional teams Deliver analytical solutions using a hypothesis-driven approach Translate complex analytical concepts into clear, actionable insights Support release activities, defect resolution, and production deployments Produce documentation, release notes, and model governance artefacts Essential Skills andamp; Experience Strong experience in machine learning and statistical modelling Exposure to fraud, financial crime, compliance, or risk analytics Experience building ML pipelines, feature engineering, and working with large datasets Hands-on experience with MLOps (CI/CD, model deployment, unit testing) Strong SQL skills Experience with model governance, explainability, and observability Experience using Azure, Azure DevOps, and Git Programming experience in Python (Scala desirable) Excellent analytical and problem-solving skills Eligible for BPSS clearance Able to work onsite in London 2-3 days per week Desirab