The Chicago White Sox is seeking to enhance model development for player evaluation, development, and strategy through advanced statistical methods
Requirements
- High proficiency in R or Python and SQL
- Deep expertise in Bayesian statistics with hands-on experience building custom Bayesian models
- Strong experience with probabilistic programming languages (Stan, PyMC, JAGS, or similar)
- Familiarity with machine learning methods (regression, classification, ensemble methods, neural networks), causal inference approaches, and core algorithms for optimization and model fitting
- Experience with version control (Git) and building production model pipelines
- Knowledge of current baseball research and sabermetrics
- Graduate degree (M.S. or Ph.D.) in Statistics or related quantitative field
Responsibilities
- Design, build, and deploy production-grade statistical models for player forecasting, evaluation, and strategic decision-making
- Own modeling projects end-to-end: research question formulation, statistical design, implementation, validation, and deployment
- Adhere to statistical best practices, coding standards, and reusable modeling infrastructure for the team
- Mentor junior analysts in advanced statistical techniques, experimental design, and model development workflows through code reviews and technical workshops
- Translate complex statistical findings into actionable insights for coaches, scouts, and front office executives
- Research question formulation, statistical design, implementation, validation, and deployment
- Build production model pipelines
Other
- Bachelor's degree in Statistics, Mathematics, Computer Science, or related quantitative field
- 3-5 years building and deploying predictive models in industry or advanced graduate training
- Strong analytical and problem-solving skills with attention to statistical rigor
- Excellent communication skills for both technical and non-technical audiences
- Ability to work independently on long-term projects and manage multiple priorities
