I turn messy, real-world data into models that make decisions. At BlackBox I cut document retrieval time 98% across 50,000+ documents; at PwC I hit 92% forecast accuracy on $20M/month in petrochemical trades. My focus is statistical rigor and pipelines built to be reproducible, not just to work once.
B.Sc. Computer Science, UMass Amherst ('26) · 3.82 GPA · Azure DP-100 & AWS AI Practitioner certified
Tools I reach for across both sides of the stack.
From raw data to a monitored, production endpoint.
Ingest
Raw data from APIs, warehouses & streams
Clean & Engineer
Handle nulls, build features
Train & Validate
XGBoost, Random Forest, cross-validation
Evaluate
Score against holdout & business metrics
Deploy & Monitor
Serve predictions, track drift
Filter by discipline, or see everything at once.
Challenge: ran an A/B test across 50+ equities (ARIMA vs. gradient-boosted forecasting), cutting RMSE 18% at p < 0.05, then served it through a Flask/FastAPI + PostgreSQL backend and a React/Plotly dashboard — cutting API response time 57% (2.3s to 1s) at 99.9% uptime.
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.
Based in New York, NY — open to Data Science / AI-ML roles.
AI Assistant