Data Engineer & Data Scientist
Building data pipelines and ML systems that turn messy data into clear decisions.
I got into data the hard way — 3rd place at the 2024 CCAC research competition for a model that quantified geographic fan bias in March Madness brackets. The math was fun. What hooked me was the engineering: cleaning 76k brackets, building features from raw geography, shipping a Streamlit app someone could actually use.
Since then I've spent two summers at Grainger — first building ETL pipelines on Airflow that cut processing time 10%, then shipping predictive models that drove inventory decisions across 11 distribution centers. Pipelines that don't break and models that move a number: that's the seam I want to keep working in.
Currently looking for the next place to do that work. If you've got a hard data problem and want it solved cleanly, let's connect.
Built predictive regression models and dashboards to optimize inventory transfer logic across 11 distribution centers.
Engineered end-to-end ETL pipelines on Apache Airflow + AWS S3, cutting data processing time by 10% and improving downstream accessibility.
Modernized technical manuals and ran financial risk analysis on engineering lifecycles, contributing to meaningful program-level savings.
RandomForest model predicting NCAA bracket choices by quantifying geographic fan bias. 67% accuracy on 76k+ brackets using Haversine distance features and KenPom analytics.
Python CLI that turns a JSON sprint spec into a fully-structured ClickUp board — Space → Folder → List → Tasks → Subtasks — in one run. Tests included.
EDA on retail shopper data — pandas/seaborn pipeline producing customer-segment insights and pairplot visualizations.
B.S. in Economics, Minor in Computer Science.
Coursework: Data Structures, Algorithms, Machine Learning, Database Systems, Software Engineering.
Won 3rd place for the March Madness Fan Predictor project — RandomForest model on 76k+ brackets.
Third place finish for undergraduate research presentation.