The A's are looking to construct statistical models that inform decision-making in all facets of Baseball Operations.
Requirements
- Proficiency in SQL, R, Python, or other similar programming languages.
- Strong understanding of modern statistical and machine learning methods, including experience with predictive modeling techniques.
- Proven experience productionizing machine learning models in cloud environments.
- Expertise in time series modeling, spatial statistics, boosting models, and Bayesian regression.
- Familiarity with integrating biomechanical data into analytical frameworks.
Responsibilities
- Design, build, and maintain predictive models to support player evaluation, acquisition, development, and performance optimization.
- Analyze and synthesize large-scale data, creating actionable insights for stakeholders within Baseball Operations.
- Research and implement advanced statistical methods, including time series modeling, spatial statistics, boosting models, and Bayesian regression, to stay on the cutting edge of sabermetric research.
- Develop and maintain robust data modeling pipelines and workflows in cloud environments to ensure scalability and reliability of analytical outputs.
- Produce clear, concise written reports and compelling data visualizations to communicate insights effectively across diverse audiences.
- Stay current with advancements in data science, statistical methodologies, and player evaluation techniques to identify and propose new opportunities for organizational improvement.
Other
- PhD in Mathematics, Statistics, Computer Science, or a related quantitative field.
- Ability to communicate complex analytical concepts effectively to both technical and non-technical audiences.
- Demonstrated ability to independently design, implement, and present rigorous quantitative research.
- Passion for sabermetric research and baseball analytics with a deep understanding of player evaluation methodologies.
- Strong interpersonal and mentoring skills with a demonstrated ability to work collaboratively in a team-oriented environment.
