Remote Senior Sports Modeling Lead

Underdog

United States

Presencial

USD 135.000 - 185.000

Jornada completa

Hace 8 días

Una candidatura completa en un minuto

Currículum y carta de presentación personalizados, listos para enviar.

Supera los filtros ATS

Ventajas ofrecidas por este puesto de trabajo

Unlimited PTO
Parental leave
Home office stipend
Virtual-first culture
Health plan

Descripción de la vacante

Underdog is seeking a seasoned Sports Modeler to join our team as a senior contributor to the quantitative engine behind our prediction market operations. You’ll own high-impact models from first research to production monitoring, collaborating with Trading and Engineering to production‑ize ideas with rigor.

You will apply Bayesian methods to synthesize internal estimates with external prices and trader observations, ensuring robust fair-value views while preserving data integrity and

Formación

  • Front-runner in sports modeling and quantitative research.
  • Proven ability to build calibrated probabilistic models for markets.
  • Strong knowledge of Bayesian methods and backtesting.

Responsabilidades

  • Design, build, and validate simulation-based predictive and pricing models across sports and markets.
  • Translate sport mechanics and data into outcome distributions and probabilities.
  • Own model lifecycle from framing to deployment and monitoring.
  • Apply Bayesian inference to update priors with new information and assess price changes.

Conocimientos

Python
Bayesian inference
GitHub
Sports modeling
Market dynamics
Code debugging
Team collaboration
AI tools

Herramientas

GitHub

Descripción del empleo

Underdog is seeking a seasoned Sports Modeler to join our team as a senior contributor to the quantitative engine behind our prediction market operations. You’ll own high-impact models from first research to production monitoring, collaborating with Trading and Engineering to production‑ize ideas with rigor.

You will apply Bayesian methods to synthesize internal estimates with external prices and trader observations, ensuring robust fair-value views while preserving data integrity and

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