Solomon T. Faro

Credit Risk ML Project – Key Achievements: Built an end-to-end credit risk ML system (data → modeling → FastAPI scoring) with full prediction logging and auditability; Developed monitoring with drift detection (PSI, KS), automated reports, and a RAG layer for interpretable, regulator-ready model explanations; Implemented production-grade MLOps: experiment tracking, model versioning, deployment automation, and dashboard-based performance monitoring.

End-to-end credit risk ML system (data → modeling → FastAPI scoring) with full prediction logging and auditability. Implemented production-grade MLOps, including experiment tracking, model versioning, deployment automation, and dashboard-based performance monitoring. Built a Retrieval-Augmented Generation (RAG) layer for interpretable, regulator-ready explanations and drift detection (PSI, KS).

Developed reproducible analytics workflow for administrative health data. Built dashboard framework integrating screening, treatment, and outcomes. Applied event-study models for policy evaluations with exportable outputs. Performed QALY-based cost-effectiveness analysis. Analyzed equity outcomes via income-stratified indicators. Delivered outputs for grants, teaching, and internal reports.