At ION Analytics, I build and operate production machine-learning systems for capital markets, including Random Forest models that rank companies for sponsor-backed exits, liquidity events, distress, and IPO investor participation. My work combines temporal feature engineering, model validation, SHAP explainability, MLflow promotion gates, and production publishing.
I've also delivered an LLM-assisted entity-resolution workflow for 400K+ fund records, co-engineered event-sourced regulatory processing across 5M+ filing records, and replaced spreadsheet-based review with an auditable Streamlit application. Earlier at ION Markets, I implemented credit-trading technology and supported traders across rates, Treasuries, derivatives, and mortgage-backed securities.
