Data Scientist - Baseball Research & Development

## Primary Purpose The Cleveland Guardians Baseball Research & Development (R&D) group is seeking to hire data scientists, either senior or entry-level. In this role, you will work alongside the rest of the R&D group to transform a treasure trove of scientific data (e.g., box scores, bat/ball/player tracking, video) into an information pipeline that helps us acquire better players, improve those players, and equip our coaches and players to emerge victorious. The Cleveland Guardians strongly prefer our teammates to reside in or relocate to Cleveland, Ohio. Exceptional candidates may be considered for remote work. We can also be flexible on start dates. ## Core Job Functions Data Scientists in our R&D group apply quantitative methods to make us better on the field. Depending on your skills and interests, your work may include: - Building statistical and/or machine learning models to guide player acquisitions, player development philosophy, and Major League decision-making. - Creating tools, prototypes, and visualizations that connect R&D insights to coaches, players, and baseball operations staff. - Communicating complex ideas clearly to both technical and non-technical audience, including executives, coaches, and players. ## Basic Requirements - Degree or demonstrated experience in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Engineering). - Proficiency in programming languages such as Python or R for data analysis and modeling. - Ability to clearly communicate complex ideas to others in both written and verbal form. - Commitment to continuous learning and solving challenging problems. - Ability to succeed in a collaborative team-oriented environment. ## Preferred Experience (not required) We are looking for a variety of skillsets. If you have experience with one or more of the following areas, you may be who we are looking for: Statistical Modeling - Advanced statistical methods (e.g., fixed effects, random effects, generalized additive models). - Bayesian statistics and probabilistic programming languages (e.g., Stan). - Database languages such as SQL.Machine Learning and AI - Large language models (LLMs). - Deep learning frameworks (e.g., TensorFlow, Torch). - High-dimensional and/or spatiotemporal data.Domain-Specific Expertise - Research in a sports context (baseball is a plus). - Game theory applied in real-world contexts.