Remote Lead Quantitative Analyst

Summary

Join a team doing cutting-edge foundational research on biomechanics, human movement, ball-flight physics, and more, with the unique opportunity to apply those findings to player evaluation.

Requirements

• 2-5+ years of relevant work or graduate school experience
• Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience
• Proficiency with scripting languages such as Python, statistical software (R, S-Plus, SAS, or similar), and databases (SQL)
• Demonstrated experience designing, constructing, implementing, and leading technical research projects for use by non-technical stakeholders
• 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

Responsibilities

• Conduct and oversee statistical forecasting projects in multiple baseball subject areas
• Collaborate with baseball subject matter experts in scouting, development, biomechanics, machine learning, decision science, and more, integrating their expertise into player evaluation models
• Maximize organizational impact of the department’s player evaluation models by advocating model-driven decision-making in various baseball contexts
• Ensure projects conform to best practices for implementing, maintaining, and improving predictive models throughout their life cycles
• Assist and mentor other members of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball and statistical modeling
• Continually enhance your and your colleagues’ knowledge of baseball and data science through documentation, reading, research, and discussion with your teammates and the rest of the front office

Preferred Qualifications

• Experience with a probabilistic programming language (Stan, PyMC, etc.)
• Experience managing or overseeing the work of other data scientists or analysts
• Experience with model-driven decision-making under uncertainty (eg. a rigorous approach to fantasy sports, poker, etc.)