Data Scientist - Sports Analytics

EXL Analytics is currently seeking a highly motivated and analytically driven Data Scientist (Econometrics) to join our sport analytics practice. This role will focus on supporting the Local Business Optimization (LBO) initiative helping sports clubs unlock new revenue opportunities and optimize local business strategies through actionable data-driven insights.

1. Collect, clean, and analyze large, complex datasets from diverse sources; streamline and integrate data collection processes and optimize query performance 
2. Design, develop, and implement advanced data science econometrics models and conduct exploration analyses to uncover trends in local market performance, fan engagement, sponsorship, ticketing, and other revenue streams.
3. Refine and enhance existing opportunity models using test-and-learn approach, including A/B testing and clustering algorithms. 
4. Develop and deploy predictive models to forecast key economic and sports business metrics (e.g., incremental revenue, sponsorship ROI, fan engagement growth).
5. Monitor developed AI/ML models for performance drift and be able to re-train degraded models when applicable. 
6. Support Club Business Development and Clubs by translating complex models into actionable analytical insights to help clubs efficiently reach their revenue opportunity. 
7. Define and track KPIs and success metrics, partnering with Club Business Development to measure program impact. 
8. Work closely with Data Engineering team for data integration and model production deployment. 
9. Present findings and recommendations in a compelling and visually engaging manner. 
10. Stay current with industry trends in sports analytics, econometrics and data science. 

Bachelor’s or Master’s degree in Econometrics Data Science, Statistics, Computer Science, or a related field. 

• 3+ years of experience in data science, analytics, or business intelligence, preferably in sports.
• Proficiency in Python, R, SQL.
• Experience with A/B testing, clustering algorithms, predictive modeling.
• Strong attention to detail with a focus on maintaining rigor across data analysis, modeling, and reporting.
• Strong communication and storytelling skills with the ability to influence stakeholders.
• Passion for sports and a deep understanding of sports leagues’ business model.

Key Skills

Technical Skills

1. Skills in statistical analysis, predictive modeling, clustering, and feature engineering. Proficient with Python, R, and SQL; experienced in working with varied data sources and ETL. 
2. Knowledge of model validation methods such as cross-validation and ROC/AUC. 
3. Familiar with modern machine learning libraries like scikit-learn, TensorFlow, and PyTorch, adhering to current best practices. 
4. Proficiency in using AWS CodeCommit for version control and collaborative code management within cloud-based development environments. 

Business & Domain Knowledge 

• Measurement & KPIs definition/tracking.
• Strategic thinking and actionable insight generation.
• Sports Business Acumen

Soft Skills

• Communication & Storytelling for technical and non-technical audiences.
• Collaboration: Experience working cross-functionally in fast-paced environments.
• Leadership, taking initiative and ownership of projects.