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Ryan McComb

Ryan McComb

Data Science Fellow in Evanston, United States

About

hey hey, my name is ryan! Most of my interests sit in some broad cross-section of elections, economics, and AI/inference. Outside of being a nerd, I swim quite a bit for my club and high school teams. In my free time I like to shoot photos, it’s a hobby of mine, and every once in a while I’ll shoot for the Chicago Union, our local pro ultimate frisbee team. I work as a Data Science Fellow at VoteHub, mostly on racecall and the occasional election model. I originally came on to help with our midterm model, including a small program that normalizes Kalshi odds.

Experience

2026 — Current

Data Science Fellow at VoteHub

Helped build a detailed election forecast and co-authored the 2026 midterm methodology paper. Developed a market-scoring pipeline and contributed to election-night racecall operations.

2025 — Current

Swim Instructor at YWCA

Taught swim lessons to young children.

2026 — 2026

Lakefront Lifeguard at Evanston Fire Department

Served as an open-water lifeguard on Evanston's lakefront. Maintained USLA, Red Cross Lifeguard, First Responder, and CPR certifications.

2025 — 2026

Volunteer Finance Lead at Daniel Biss for Congress (IL-9)

Supported volunteer finance operations for a competitive congressional primary. Provided data analysis and photography for campaign materials.

Projects

A forecasting aggregator for the IL-9 Democratic primary combining prediction markets, polling, and fundraising data.

FUNDY

A Bayesian model predicting candidates' end-of-quarter fundraising from daily itemized FEC data.

Seiche

A live model forecasting Lake Michigan water temperature using quantile gradient-boosting models.

Manifold Prediction Market Trader

Active trader ranked in the top 50 by volume and top 0.25% by profit.

Education

2024 — 2028

High School Diploma at Evanston Township High School

Skills

Election ForecastingPrediction MarketsData JournalismMachine LearningStatistical ModellingCampaign Finance AnalysisTopographic MappingLLM Fine-tuning