AI Enablement & Implementation | Product Operations | Business Analytics
I translate ambiguous workflows into governed AI-enabled systems, measurable decision support, and practical implementation.
One evolving umbrella project spanning orchestration, memory and retrieval, evaluation and governance, reliability and observability, and analytics. The system is privately maintained; this profile describes only its architecture and operating principles, without unpublished performance claims.
A reproducible R time-series project comparing ETS and seasonal ARIMA models with rolling-origin validation.
A team-based Kaggle project. I built and iterated the repository's modeling pipeline from preprocessing and cross-validation through leakage checks, XGBoost, CatBoost, and the final ensemble submission.
An R-based visual analysis of how venture investment patterns shifted around the 2008 financial crisis.
- Define acceptance criteria before implementation.
- Preserve source evidence and make uncertainty visible.
- Separate generation from evaluation.
- Keep consequential actions behind human review.
MS Business Analytics candidate at Drexel University's LeBow College of Business, expected December 2026, with experience across revenue operations, real-estate operations, analytics, and implementation.