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Sports Analytics Manager Jobs (NOW HIRING)

PFF

Senior Data Engineer

$120K - $160K/yr

About PFF PFF is a leading sports analytics company that transforms complex football data into ... Strong experience with building and managing data ingestion pipelines and ETL/ELT processes in a ...

Omnicom Media US

Manager, Research

New York, NY · On-site

Undergraduate degree required * 3-5 years of experience in sports media, sports analytics or similar field * High level of proficiency/interest in data cleaning, management, and analysis * Experience ...

Onyx Games LLC

Project Manager

New York, NY · On-site

Interest in sports analytics and predictive modeling is highly desirable Tools & Technologies * Project management tools (Jira, Asana, ClickUp, or similar) * Collaboration tools (Slack, Notion ...

Omnicom Media US

Manager, Data and Insights

New York, NY · On-site

Undergraduate degree required * 3-5 years of experience in sports media, sports analytics or similar field * High level of proficiency/interest in data cleaning, management, and analysis * Experience ...

Uofl

Successful teaching experience in at least one of the following content areas: statistics, sport science/sport analytics, athlete monitoring and data management * Experience with statistical software ...

Paramount

Yield Manager, Yield Analytics

Hollywood, CA · On-site

$80K - $90K/yr

Yield brings analyses and insights to people at all levels of the Paramount organization, but its ... Involvement in Paramount vast array of products - Paramount+, Pluto, CBS News Digital, Sports HQ ...

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How much do sports analytics manager jobs pay per year?

As of Jun 29, 2026, the average yearly pay for sports analytics manager in the United States is $55,952.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,000.00 and $60,000.00 per year, depending on experience, location, and employer.

What is a sports analytics manager?

A sports analytics manager is responsible for analyzing data related to athletic performance, team strategies, and game statistics to inform decision-making. They often use statistical software and data visualization tools, and typically have a background in sports science, statistics, or data analysis. Their role supports coaching staff and management in improving team performance and strategic planning.

What does a Sports Analytics Manager do?

A Sports Analytics Manager is responsible for collecting, analyzing, and interpreting data related to athletic performance, player statistics, and team strategies. They use advanced statistical methods and software to help teams make informed decisions about player recruitment, game tactics, and injury prevention. By turning complex data into actionable insights, they play a crucial role in enhancing a team's competitive edge. These professionals often collaborate with coaches, scouts, and executives to implement data-driven strategies across the organization.

What is the highest salary for a sports analyst?

The highest salaries for sports analysts can exceed $100,000 annually, especially for those with extensive experience, advanced analytics skills, and work with major sports organizations or media companies. Senior roles or those with specialized expertise in data modeling and visualization tools tend to command higher compensation.

What is the highest paying job in sports management?

The highest paying jobs in sports management typically include executive roles such as Sports Franchise Owner, General Manager of a major team, or Chief Operating Officer, with salaries often exceeding several million dollars annually. These positions require extensive experience, leadership skills, and industry connections, and they often involve overseeing large organizations or investments in sports teams or leagues.

What is the difference between Sports Analytics Manager vs Sports Data Analyst?

AspectSports Analytics ManagerSports Data Analyst
Required CredentialsBachelor's or Master's in Sports Management, Statistics, or related field; experience in analyticsBachelor's in Statistics, Data Science, or related field; strong analytical skills
Work EnvironmentLeads teams, manages projects, strategic planningAnalyzes data, prepares reports, supports decision-making
Employer & Industry UsageSports teams, leagues, sports analytics firmsSports teams, media outlets, analytics companies
Search & Comparison IntentUnderstanding managerial roles in sports analyticsEntry to mid-level data analysis roles in sports

The Sports Analytics Manager oversees analytics teams and strategic initiatives, while the Sports Data Analyst focuses on data collection, analysis, and reporting. The manager role involves leadership and planning, whereas the analyst role emphasizes technical data work. Both roles require strong analytical skills and relevant education, but differ in scope and responsibilities.

What are the key skills and qualifications needed to thrive as a Sports Analytics Manager, and why are they important?

To thrive as a Sports Analytics Manager, you need strong quantitative analysis skills, a background in statistics or data science, and typically a relevant degree such as in mathematics, statistics, or sports management. Familiarity with data analysis tools like R, Python, SQL, and sports analytics software is crucial, along with experience in data visualization platforms. Exceptional communication, problem-solving, and leadership abilities help translate complex data into actionable insights for coaches and executives. These skills drive data-informed decisions that enhance team performance, strategy, and competitive advantage.

What are some common challenges faced by a Sports Analytics Manager when integrating data-driven insights into coaching decisions?

A Sports Analytics Manager often encounters challenges when translating complex data insights into actionable recommendations that coaches and athletes can easily understand and trust. Bridging the gap between technical analytics and practical application requires strong communication skills and ongoing collaboration with coaching staff. Additionally, gaining buy-in from team members who may be skeptical about analytics, and ensuring data quality and relevance, are frequent hurdles. Overcoming these challenges involves building strong relationships, providing clear and compelling data visualizations, and demonstrating the tangible impact of analytics on team performance.

Will sports analytics be taken over by AI?

Sports Analytics Managers use AI and machine learning tools to analyze player performance, game strategies, and injury risks. While AI automates data processing and enhances insights, human expertise remains essential for interpreting results and making strategic decisions in sports analytics.
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Sports Analytics Manager jobs near you

Quantitative Developer - New Graduate

Dime Line Trading

Chicago, IL

Full-time

Posted 12 days ago

Job description

What you'll do:
  • Contribute to the research, design, and implementation of predictive statistical and machine learning models.
  • Prototype and backtest models, monitor performance, and assist with optimizations.
  • Contribute to key feature development for model efficiency
  • Develop and maintain Python codebases in a Linux environment.
  • Help to design and implement new pricing models and frameworks
  • Support data pipeline and SQL database interactions for real-time models.
  • Assist in improving trading systems and operational tools.
  • Gain exposure to multiple sports, quantitative disciplines, and production engineering.
  • Other duties as assigned.
Skills you'll need:
  • Proficiency in Python (experience in R or other languages a plus).
  • Strong interest in statistical modeling, machine learning, or predictive analytics.
  • Familiarity with Linux and SQL databases.
  • Ability to work in a fast-paced environment and manage multiple tasks.
  • Interest in sports and sports analytics / sabermetrics.
  • Strong problem-solving and communication skills.
  • Predictable and reliable availability
It's great to see:
  • Coursework in statistics, optimization, computer science, or related fields.
  • Prior internship or project experience in trading, quantitative research, or software engineering.
  • Exposure to object-oriented development, real-time systems, or algorithmic trading models.
  • Experience with sports gambling, fantasy sports, or predictive analytics applied to sports.