Tennis Data Scientist: Real-Time ML Analytics Lead

7Seventy; United States; Hybrid; USD 135,000 - 190,000; Full time

Job summary

University of Michigan Health-West is seeking a Tennis Data Scientist to develop predictive sports analytics products, focusing on ML and statistical models for real-time analytics and betting solutions.

You will own core modeling work and collaborate with data science, data engineering and product teams, contributing from proof-of-concept to production deployment with rigorous offline and online experimentation.

Qualifications

• Master’s degree in a technical field such as Data Analytics, Data Science, CS.
• At least 2 years producing production-scale models in tennis or sports betting.
• At least 5 years building ML or statistical models for sports or betting.
• Strong knowledge of probability, ML, statistics, Bayesian methods, and MCMC.
• Experience with SQL and Python in analytics environments.
• Experience with GitHub and CI/CD processes.
• Experience in AWS.

Responsibilities

• Design, develop, and improve ML and statistical models for advanced sports analytics and betting products.
• Create contextualized feature sets using tennis domain knowledge.
• Contribute to all stages of model development from POCs to production deployment in collaboration with engineering and product teams.
• Improve model performance through offline and online experimentation.
• Analyze outputs to identify weaknesses and guide future development efforts.
• Follow software engineering best practices and document modeling approaches and results.

Skills

Machine learning

Python

SQL

AWS

GitHub

Communication

Leadership

Education

Master's degree in Data Analytics/Data Science/CS

Tools

GitHub

CI/CD

AWS

Job description

Base Salary Range: $135,000 – $190,000 per year

About the Role

This opportunity is for a Tennis Data Scientist supporting the development of predictive sports analytics products. The role focuses on building and improving machine learning and statistical models that power real-time sports betting and analytics solutions.

This position offers meaningful ownership over core modeling work and involves collaboration across data science, data engineering, and product teams. The work requires strong technical depth, sports-specific feature development, experimentation, and production-minded model delivery.

What You’ll Do

• Design, develop, and improve machine learning and statistical models that support advanced sports analytics and betting products.
• Create contextualized feature sets using sport-specific domain knowledge related to tennis.
• Contribute to all phases of model development, including proof-of-concepts, beta testing, and production deployment in partnership with engineering and product teams.
• Continuously improve model performance through rigorous offline and online experimentation.
• Analyze model outputs and performance results to identify weaknesses and guide future development efforts.
• Follow software engineering best practices and contribute to shared code repositories.
• Document modeling approaches, results, and recommendations for both technical and non-technical stakeholders.

Qualifications

• Master’s degree in Data Analytics, Data Science, Computer Science, or a related technical field.
• At least 2 years of experience developing production-scale models for tennis or sports betting applications.
• At least 5 years of experience building and delivering machine learning and/or statistical models that solve business problems in sports or sports betting.
• Strong expertise in probability theory, machine learning, inferential statistics, Bayesian statistics, and Markov Chain Monte Carlo methods.
• Experience with SQL and Python in analytical and modeling environments.
• Experience with source control tools such as GitHub and related CI/CD processes.
• Experience working in AWS environments.
• Demonstrated leadership skills and the ability to collaborate across teams to solve complex problems with innovative solutions.
• Strong communication skills for presenting to both technical and non-technical audiences.

Additional Information

• Employment is subject to applicable background and reference checks.
• Role responsibilities may evolve based on business needs.

Recruitment notice: all communication only happens from authorized email addresses ending [e-mail]. We never ask for payments or sensitive documents. Suspect a scam? Email [e-mail]