Data Scientist – Underwriting Analytics
Summary
Builds and maintains pricing and forecasting models for insurance underwriting using Python/R, statistical ML, and BI tools like Power BI.
- Leading the development of analytical solutions and models to support underwriting and pricing decisions.
- Designing, building and maintaining robust data pipelines, datasets and codebases that support regular MI, dashboards and advanced analytics.
- Taking ownership of more complex reporting and forecasting processes, ensuring they are accurate, scalable and well‑documented.
- Enhancing existing MI for Underwriting and Performance KPIs, introducing more advanced analytics.
- Developing and maintaining financial and performance forecasting models to support planning and portfolio steering.
Requirements
- Bachelor’s degree (or equivalent) in Mathematics, Statistics, Actuarial Science, Data Science, Computer Science, Engineering, Economics, Finance or a related quantitative discipline.
- Typically 2–5 years of experience in data science, advanced analytics, actuarial, or a closely related field.
- Strong coding skills in at least one of Python or R.
- Practical experience with statistical modelling and/or machine learning techniques, such as: Generalised linear models (GLMs) or other regression methods, Tree-based methods (e.g. random forests, gradient boosting), Time series modelling and forecasting, Clustering and segmentation technique.
- Experience with data visualisation tools such as Power BI or Tableau.