Job description
Join a premier professional sports organization committed to excellence in performance and fan experience. As the Senior Sports Analytics Lead, you will shape the dataâdriven strategy across performance staff, medical, and coaching teams in Los Angeles. You will architect analytics solutions, translate complex data into actionable insights, and contribute to elite athlete development.
We offer a dynamic, fastâpaced environment with worldâclass facilities and a team of professionals dedicated to innovation in sport.
Responsibility
- Lead the design and optimization of the performance analytics pipeline using Python, SQL, and leading visualization tools to deliver timely, actionable insights.
- Collaborate with coaches, sports scientists, and medical staff to translate data into training plans, recovery protocols, and load management decisions.
- Build and maintain interactive dashboards to monitor player load, training response, performance trends, and injury risk indicators.
- Ingest and harmonize data from GPS/tracking devices, wearables, HRV sensors, and performance tests while ensuring data quality and governance.
- Develop predictive models to forecast performance trajectories and mitigate injury risk; quantify uncertainty and validate results with stakeholders.
- Communicate complex analytics findings clearly to coaching staff and executive leadership, tailoring delivery to backgrounds and needs.
- Mentor a team of analysts, promote best practices in reproducible research, and drive continuous improvement in analytics capabilities.
Qualification
- Bachelor's degree in a quantitative field (Statistics, Data Science, Computer Science, Kinesiology) with a preferred Master's or PhD.
- 5+ years of experience in sports analytics or a closely related field within professional sports or high-performance environments.
- Proficiency in Python or R, SQL, and data visualization tools (Tableau, Power BI, or equivalent).
- Experience with wearable and GPS data, HRV, load monitoring, and biomechanical metrics.
- Strong statistical foundation and experience with predictive modeling, ML methods, and hypothesis testing.
- Excellent communication and collaboration skills; ability to present complex insights to diverse audiences and work under high-pressure deadlines.
- Knowledge of data governance, code versioning (Git), and best practices for reproducible research.