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Data Science Sports Jobs (NOW HIRING)

Swish Analytics

Soccer Data Scientist

San Francisco, CA · On-site +1

$130K/yr

Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area * Demonstrated experience developing models at production scale for Soccer , or sports betting for ...

Swish Analytics

NFL Data Scientist

San Francisco, CA · On-site +1

$140K/yr

Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area * Demonstrated experience developing models at production scale for NFL, CFB, or sports betting for ...

Swish Analytics

Soccer Data Scientist

San Francisco, CA · On-site

$130K/yr

Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area * Demonstrated experience developing models at production scale for Soccer , or sports betting for ...

Swish Analytics

NFL Data Scientist

San Francisco, CA · On-site

$140K/yr

Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area * Demonstrated experience developing models at production scale for NFL, CFB, or sports betting for ...

Swish Analytics

Data Scientist

San Francisco, CA · On-site +1

$150K/yr

Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the ... This role will work at the intersection of data science, trading, and data engineering to help ...

Swish Analytics

NFL Data Scientist

San Francisco, CA

$140K/yr

Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area * Demonstrated experience developing models at production scale for NFL, CFB, or sports betting for ...

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Data Science Sports information

What is a Data Science Sports?

A Data Science Sports job involves using data analytics, machine learning, and statistical models to extract insights from sports-related data. Professionals in this field analyze player performance, team strategies, injury prevention, and fan engagement to help teams, coaches, and organizations make data-driven decisions. They work with large datasets, build predictive models, and create visualizations to uncover trends and patterns. This role is essential in modern sports for optimizing performance, scouting talent, and enhancing the overall fan experience.

What does a Data Science Sports professional do?

In a Data Science Sports role, your daily tasks often involve extracting, cleaning, and analyzing large sets of sports-related data to uncover performance trends or predictive insights. You’ll likely collaborate closely with coaches, scouts, and other team members to translate your findings into strategic recommendations. Creating data visualizations, building predictive models, and presenting reports to non-technical stakeholders are also common parts of the job. This dynamic environment requires adaptability and a passion for both sports and data-driven problem solving.

What skills and qualifications are needed for a Data Science Sports position?

To thrive in Data Science Sports, you need strong analytical skills, statistical knowledge, and experience with sports data combined with a background in mathematics, statistics, or computer science. Familiarity with programming languages like Python or R, data visualization tools, machine learning platforms, and relevant sports analytics software is highly valuable. Effective communication, problem-solving abilities, and a collaborative mindset are crucial soft skills in this field. These competencies enable professionals to turn complex sports data into actionable insights that drive decision-making for teams, coaches, and organizations.

How much do data science sports make?

Data science roles in sports typically have salaries ranging from $60,000 to over $120,000 annually, depending on experience, location, and the level of responsibility. Professionals often use skills in statistics, machine learning, and data visualization tools to analyze athletic performance, team strategies, or fan engagement. Entry-level positions may start lower, while senior roles or those in major leagues tend to pay higher salaries.

What do data science sports do in sports?

Data science professionals in sports analyze large datasets to improve team performance, player health, and game strategies. They use statistical models, machine learning, and data visualization tools to identify patterns and inform decision-making in areas like player recruitment and game tactics.
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Data Science Sports jobs near you
Infographic showing various Data Science Sports job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Sr Manager Data Science and AI Platform Enablement

Academy Sports + Outdoors

Katy, TX • On-site

Full-time

Posted 22 days ago

Academy Sports + Outdoors rating

5.8

Company rating: 5.8 out of 10

Based on 439 frontline employees who took The Breakroom Quiz

428th of 734 rated retailers

Job description

Who We Are
At Academy Sports + Outdoors our vision is to be the best sports + outdoors retailer in the country - but what truly sets us apart is our people. We're a passionate, purpose-driven team that's as committed to each other as we are to our customers.
We've spent over 80 years building a culture that puts people first. We believe in creating opportunities for growth, fostering meaningful connections, and supporting every Team Member's journey. What fuels us? Our belief in the power of fun.
Here, you won't just help customers gear up for their next adventure - you'll launch one of your own. Whether you're starting out or leveling up, Academy is a place where fun can't lose!
Education:
  • Bachelor's in engineering, Statistics, Data Science, or Computer Sciences
  • Master's degree in Analytics or data science (preferred)

Work Experiences:
  • 8+ years of experience in data science, advanced analytics, applied machine learning, or related fields. Experience in retail or B2C is preferred
  • 4+ years of experience leading teams or major technical initiatives, including people management or matrixed leadership
  • 3+ years of experience in B2C, retail, e-commerce, marketing analytics, or customer-facing analytics domains
  • 3+ years of experience building, deploying, or enabling production-grade analytics or ML solutions

Skills:
  • Strong engineering mindset with experience in: Modular code, Reproducibility & Production-grade analytics or ML systems
  • Experience working with digital analytics, customer data platforms, or experimentation data (e.g., Adobe, web/app analytics, or similar ecosystems)
  • Proven ability to influence across matrixed organizations without direct ownership
  • Experience partnering with data engineering, platform, and governance teams
  • Ability to balance speed to value with long-term scalability
  • Experience enabling MLOps or analytics platforms, preferred
  • Exposure to customer analytics, personalization, marketing, or e-commerce use cases, preferred
  • Experience operating in early-to-mid maturity data organizations, preferred
  • Comfort shaping standards without formal authority, preferred

Responsibilities:
Platform & AI Standards Enablement
  • Define and evolve reusable data, feature, and modeling patterns, MLOps and model lifecycle standards, and production-ready analytics/ML solutions.
  • Partner with CIO Data Engineering and Platform teams to influence canonical customer data models, shared datasets, feature reuse, and data access/consumption standards.

Customer Domain Translation & Enablement
  • Translate customer, marketing, and omnichannel needs into scalable technical and platform-aligned patterns.
  • Enable domain-aligned data scientists, analysts, and engineers to adopt standards, reduce reinvention, and accelerate delivery.
  • Influence enterprise priorities through evidence, design proposals, and proof-of-value work.

Governance, Quality & Responsible AI
  • Represent customer-domain data needs in governance forums.
  • Help evolve governance standards that are practical for personalization, experimentation, and customer analytics.
  • Ensure quality, fairness, trust, and compliance are embedded by design.

Customer Data Domain Enablement
  • Lead Adobe Analytics and Quantum Metrics data capture, acting as a catalyst for effective use across analytics, personalization, and experimentation.
  • Serve as the data domain owner for all customer-facing data domains, including tag management, Bridg, and clean room capabilities, accountable for data definitions, quality, access, and downstream usability.
  • Partner with IT pods, platform, and engineering teams to define and govern customer data flows securely and at scale through well-documented integrations.

Leadership Attributes
  • Systems thinker with a pragmatic bias toward delivery.
  • Trusted by both business and technical stakeholders.
  • Comfortable letting teams execute independently while influencing across a matrixed organization.
  • Optimizes for scale and reuse, not personal ownership.

Physical Requirements & Attendance:
  • Acceptable level of hearing and vision to perform job duties
  • Adhere to company work hours, policies, procedures, and rules governing professional staff behavior
  • Regular attendance in the office is required
Equal Employment Opportunity
Academy is an Equal Opportunity Employer and does not discriminate with regard to employment opportunities or practices on the basis of race, religion, national origin, sex, age, disability, gender identity, sexual orientation, or any other category protected by law.

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