Overview
Key Responsibilities (including but not limited to):
1. Own the end-to-end modelling lifecycle: problem framing, data build, feature engineering, model development, validation, documentation
2. Build and maintain risk and price models using GLMs and machine learning
3. Translate models into implementable rating structures
4. Strong governance: change control, champion–challenger/shadow runs, rollback plans, and clear approvals and audit trails
Experience required:
5. Strong general insurance pricing toolkit: GLMs (Poisson/NB/Tweedie), GAMs, credibility/hierarchical methods; experience with tree-based ML (GBM/XGBoost/CatBoost) and regularisation
6. Proficient in R and Python, with strong SQL; comfortable in Git-based workflows and “in the engine room” with proprietary rating systems
7. Hands-on experience taking models from concept to live in rating engines; robust validation, change control and post-live monitoring
8. Familiarity with peril/exposure enrichment relevant to home insurance (e.g. flood and subsidence datasets) and geospatial modelling considerations
9. Awareness of reserving concepts, claims inflation and their interaction with technical pricing
10. Knowledge of model risk management, documentation standards and governance under UK regulation (Consumer Duty, Fair Value Assessments, GIPP)