• Designed and implemented end-to-end data pipelines to aggregate and normalize geospatial risk datasets from multiple public sources, creating address-level features used in underwriting analysis.

• Built and optimized ML-based risk scoring models (CatBoost, XGBoost), improving regional hazard score accu- racy by 15% and enabling transparent, auditable underwriting decisions.

• Designed a Retrieval-Augmented Generation (RAG) pipeline to produce grounded, explainable risk narratives, reducing manual review time for underwriters.

• Built a FastAPI service to deliver JSON risk reports with configurable parameters and error handling.

• Used data science to improve transparency and support data-driven decision-making.

• Analyzed 25K+ financial contracts, uncovering discrepancies and boosting fraud detection.

• Coordinated 30+ stakeholder meetings, helping translate data insights into clear recommendations for decision-makers.