Sports Data Scientists apply statistical modeling, machine learning, and data engineering to sports performance, scouting, injury prevention, and fan engagement. They work with player tracking data, biometric sensors, game footage, and historical statistics to uncover insights that inform coaching decisions, draft strategy, and in-game tactics. This role blends deep technical skill with sports domain knowledge, often requiring collaboration with coaches, scouts, and front-office executives who may not have technical backgrounds.
| Entry level | $58,000 |
| Median | $95,000 |
| Senior | $140,000 |
| Top 10% | $190,000 |
| Job growth | +22% |
| Professionals in the USA | 15,000 |
| Typical hours/week | 45 hrs |
| Remote work share | 20% |
| Annual job openings | 2,500/yr |
| Demand | High |
AI and machine learning tools are automating routine statistical analysis, data cleaning, and basic performance reporting in sports analytics. However, the role is evolving toward higher-value work like strategic interpretation, model building, and communicating insights to coaches and executives who need context, not just numbers.
Automation exposure: Automated data collection, basic statistical summaries, video tagging, injury risk flagging, and standard visualization dashboards are increasingly handled by AI systems and specialized sports tech platforms.
The human edge: Understanding team dynamics, translating complex models into actionable coaching strategies, building trust with athletes and staff, and making judgment calls that blend data with sport-specific intuition remain distinctly human strengths.
Figures are estimates for exploration — verify current data with BLS.gov.