Data Scientist & AI/ML Engineer.

Challenge: walk-forward validated ARIMA vs. gradient-boosted (XGBoost) forecasting across 59 equities — 33.6% RMSE improvement at p ≈ 3×10⁻⁹ — then served it through a FastAPI + PostgreSQL backend and a React/Plotly dashboard, deployed live with sub-second API response times.

Challenge: built the eval harness and a hand-verified 42-question golden set before writing a single retriever, then ran a 3-stage retrieval ablation (BM25 → dense → RRF hybrid fusion) over 109 SEC filings, NAIC model laws, and state insurance bulletins, scored with bootstrap confidence intervals. Hybrid fusion led on every metric (recall@10 0.767, MRR 0.495, nDCG@10 0.556); refusal accuracy held at 91.7%, and hand-tracing every false refusal showed all 9 were retrieval-recall gaps, not generation failures.

Challenge: built an NLP pipeline (TF-IDF, t-SNE, K-means, LDA) to cluster 32,000+ CORD-19 research papers, preserving 95% variance while reducing a multi-thousand-paper corpus into navigable topic groups. (Lumiere Education Research)

Challenge: cut document retrieval time 98% (30 min to <1 min) across 50,000+ documents with a statistical ranking pipeline, and automated resume fitment scoring for ~500 applications/cycle with a TF-IDF-based NLP model — reducing screening time 60%.

Challenge: hit 92% forecast accuracy on $20M/month in petrochemical (Paraxylene) trades — cutting forecast error from 45% to 28% with XGBoost + Random Forest — then containerized the model with Docker and deployed on AWS SageMaker, scaling real-time inference to 10,000+ records/day.