I build machine learning models and applied AI systems that translate complex data into decisions people can actually use from unsupervised clustering to LLM-powered analytics, end to end.
Applied machine learning across real business problems. Each project is built end to end from messy data through to a result a decision-maker can use.
Leveraging large language models to analyze phone and web chat transcripts, identifying patterns that lead to poor customer outcomes. The pipeline ingests raw transcript data, uses LLM-based classification to flag negative interactions, and surfaces actionable signals for operations teams to improve agent performance and customer experience at scale. Built end to end in Python.
Clustering 8,887 properties by physical characteristics to identify distinct property types for price benchmarking, independent of location labels.
An AI-powered query engine that translates plain English questions into executable SQL using GPT-3.5, applied to Amazon product and review data hosted in PostgreSQL.
End-to-end credit risk pipeline predicting the likelihood of loan default using borrower and loan-level features, with SHAP-based explainability to surface the drivers behind each risk score.
Clustering WNBA players by playing style, predicting game outcomes, and identifying potentially mispriced betting lines using neural network models.
Clustering return transactions to detect patterns indicative of return fraud, surfacing high-risk segments and behavioral signals to support loss prevention teams.
Applying PCA to survey and behavioral data to identify latent dimensions of political distrust, revealing distinct population segments and their underlying attitudes toward institutions.
Python is my primary working language, with additional depth in R and SQL. Every tool chosen for the problem, not for novelty.