Nathan Tilley

Initially worked on the Life side of the Insurance Consulting and Technology department, and later transitioned to the Property & Casualty (P&C) side.

Highlights include:

• Contributed to the development and testing of a General Liability model build using WTW Radar.
• Conducted a data enrichment exercise for home insurance pricing models, collating together multiple third-party data sources to improve the granularity of inputs.
• Applied machine learning techniques, including k-means clustering and DBSCAN, to investigate patterns in customer data sourced from a market comparison website.
• Played a significant role in improving a team's processes by developing and rigorously testing complex Python scripts that enhanced efficiency and accuracy.
• Assisted with the development of multiple Python models for the investments sub-team, converting their Excel models into Python scripts and integrating results into Power BI dashboards for real-time data visualization.
• Carried out data analysis and processing of member data from a child trust fund scheme, followed by fitting the data to a GLM to predict final fund balances.
• Conducted in-depth data analysis, including marital status analysis, actual vs. expected (AvsE) analysis, and a postcode-based mortality analysis, as part of my work with the Bulk-Purchase Annuities (BPA) team.