Data Scientist · Ex-Deloitte · New York
I build end-to-end data projects — from sourcing and cleaning through modeling and interpretation — with a focus on financial and economic data, time series, and machine learning.
Pulled and analyzed CFTC disaggregated COT data via the Socrata API. Built trader-class net positioning metrics for Producers, Swap Dealers, and Managed Money, and produced interpretable visualizations for market structure analysis.
Time series analysis and forecasting of weekly initial unemployment insurance claims. Applied classical statistical methods to model trends, seasonality, and produce forward-looking estimates of labor market conditions.
Used unsupervised machine learning to identify natural groupings of chess player skill levels. Applied K-Means clustering and related techniques to behavioral features extracted from game data.
Python, SQL, R, SAS
Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Regression, Classification, Clustering, Time Series, A/B Testing
Git, Jupyter, Conda, REST APIs, JSON
I'm a data scientist based in New York with experience at Deloitte and in government analytics. I care about building things that work — clean pipelines, clear visualizations, and models grounded in real data. When I'm not working with data, I'm usually reading about markets or playing chess.