Quantitative Analyst Associate (2027)

As a Quantitative Analyst (QA) Associate, you help shape The Phillies Baseball Operations strategies by processing, analyzing, and interpreting large and complex data. You do more than just crunch the numbers; you carefully plan the design of your own studies by asking and answering the right questions, while also working collaboratively with other analysts and software engineers on larger projects.

Using analytical rigor, you work with your team as you mine through data and see opportunities for The Phillies to improve. After communicating the results of your studies and experiments to Baseball Operations leadership and executive staff, you collaborate with front office executives, scouts, coaches, and trainers to incorporate your findings into Phillies practices. Identifying the challenge is only half the job; you also work to figure out and implement the solution.

• →Conduct statistical research projects and manage the integration of their outputs into our proprietary tools and applications (e.g., performance projections, player valuations, draft assessments, injury analyses, etc.)
• →Communicate with front office executives, scouts, coaches, and medical staff to design and interpret statistical studies
• →Assist the rest of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball, statistics, data visualization, and programming
• →Continually enhance your knowledge of baseball and data science through reading, research, and discussion with your teammates and the rest of the front office
• →Provide input to database architecture to ensure efficient application of baseball data

• Deep understanding of statistics, including supervised and unsupervised learning, regularization, model assessment and selection, model inference and averaging, ensemble methods, etc.
• Meaningful experience programming, using analytical software (Python, R, or similar), and interacting with databases
• Proven willingness to both teach others and learn new techniques
• Willingness to work as part of a team on complex projects
• Proven leadership and self-direction

• Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience
• 0-5+ years of relevant work experience
• Experience drawing conclusions from data, communicating those conclusions to decision makers, and recommending actions

In player evaluation, some metrics are highly predictive of future performance but provide limited information about why a player will succeed or fail. Other metrics may be less predictive on their own but can help identify specific strengths, weaknesses, or opportunities for improvement.

Assume you have access to several years of professional baseball data, including traditional statistics, pitch- or play-level tracking data, scouting evaluations, player demographics, injury history, and minor-league level and park context.

In 250 words or less, describe how you would determine which information should be included in a player projection model and which information should instead be used primarily to explain, diagnose, or contextualize the projection. Discuss how you would evaluate a metric that improves historical model accuracy but may not remain stable over time, may duplicate information contained in other variables, or may be difficult to obtain consistently for all players.