• Own and continuously improve the end-to-end reserving process, including maintaining and enhancing Python-based reserving models to improve accuracy and efficiency. • Develop and deploy predictive models using telematics, claims, and policy data to improve pre-bind and post-bind risk selection, with measurable impact on ULR and cancellation performance. • Build and refine large loss models using claims notes and historical data to improve early identification of high-risk claims. • Design and implement dynamic pricing frameworks and capabilities leveraging live market and portfolio data. • Translate complex data insights into clear actionable business recommendations for underwriting, growth, and leadership teams. • Produce carrier-level reporting deliverables and analytical outputs that meet high-quality and compliance standards. • Create agency-level and geographic performance insights to guide growth strategy and strategic initiatives. • Automate recurring analyses and reporting workflows to increase speed, scalability, and reliability.