Quantitative Sports Modeler

At Underdog, we make sports more fun.

Our thesis is simple: build the best product for sports fans and we will win the space. Founded in 2020, we're one of the fastest-growing sports companies ever, and there’s still so much left to do. We've built and scaled products across fantasy sports, sports betting, and prediction markets, all united in one app that’s seamless, intuitive, and actually fun to use.

Underdog isn’t for everyone. One of our core values is give a sh*t. The people who win here are the ones who care, push, and perform. We’re remote-first by design, but highly collaborative with regular in-person gatherings that help teams build, iterate, and move fast together.

Got that dog in you? Join us.

After all, winning as an Underdog is more fun.

What you'll do:

As a Sports Modeler at Longshot Capital, you will be a senior individual contributor on the team responsible for the quantitative engine behind our prediction market trading operation. Reporting to the Senior Manager, Pricing, you will own high-impact models from initial research through production monitoring, while helping set the technical standard for the wider modeling group. Your work will be tested in live markets and evaluated through calibration, market quality, and trading performance.

This role is for someone who combines deep sports modeling expertise with strong market judgment. You will use Bayesian methods to synthesize internal model estimates, sportsbook prices, exchange market information, and trader observations into the best available view of fair value. You will work hands-on with Trading and Engineering, using AI tools aggressively and responsibly to shorten the path from idea to production without lowering the bar on rigor or correctness.

Build Simulation Based Sports Models

•     Design, build, and validate simulation-based predictive and pricing models across multiple sports, market types, and time horizons, including pregame and live opportunities where appropriate.
•     Translate sport mechanics, team and player data, correlations, and uncertainty into full outcome distributions and market probabilities rather than relying only on point estimates.
•     Own the model lifecycle from problem framing, data and feature design through backtesting, calibration, deployment, documentation, and ongoing production monitoring.
•     Apply Bayesian inference to establish and update priors as new information arrives, with a clear understanding of when the evidence supports a meaningful change in price.

Combine Model Outputs with Market Information

•     Understand how sportsbook prices are formed, including margin, market limits, timing, line movement, and differences in information quality across operators.
•     Interpret exchange prices and order books in the context of liquidity, spread, depth, flow, adverse selection, inventory, and other market-microstructure effects.