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Remote Data Science Sports Jobs (NOW HIRING)

Swish Analytics

Data Scientist

San Francisco, CA · Remote

$150K/yr

Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the ... This position is remote from the USA or Canada. Duties: * Develop infrastructure for trader ...

Quadax, Inc.

Data Science Analyst

Middleburg Heights, OH · On-site +1

Open to remote employees ONLY in: OH (if residence is outside a reasonably commutable distance), PA ... a Analyst or in Data Science * Strong Python programming skills and experience with common ML ...

FocusKPI Inc.

Data Science Intern

Boston, MA · Remote

Data Science Intern Statistical Modeling & Marketing Measurement Remote | Internship | Full-time | 3 months About the Role We are looking for a curious and analytically minded Data Science Intern to ...

FocusKPI Inc.

Data Science Intern

Boston, MA · On-site +1

Data Science Intern Statistical Modeling & Marketing Measurement Remote | Internship | Full-time | 3 months About the Role We are looking for a curious and analytically minded Data Science Intern to ...

Zenlaw

Data Scientist (Remote)

New York, NY · On-site +1

Our full-stack Data Science Team uses Python for research and development. Our wide range of ... Due to the remote nature of this role, we are unable to provide visa sponsorship.

The Knot Worldwide

VP, Data Science

The VP of Data Science & Analytics will lead experimentation, business intelligence, and advanced ... This role is fully remote and not tied to any specific office location. While there are no regular ...

YO AI Labs

AI Data Scientist - Remote

New York, NY · Remote

AI Data Science Domain Expert Job Type: Contractor (Part-Time) Location: Remote Job Overview We are seeking experienced AI Data Science Domain Experts to contribute their expertise to an innovative ...

YO AI Labs

Data Scientist Expert - Remote

New York, NY · Remote

Contractor Location: Remote About the Role We are looking for experienced data scientists and ... Evaluate AI outputs across data science, statistics, ML, and quantitative problems. * Create expert ...

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Must have a Advanced Degree (Master s or PhD) in Statistics, Applied Mathematics, Data Science ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Must have a Advanced Degree (Master s or PhD) in Statistics, Applied Mathematics, Data Science ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

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Remote Data Science Sports information

What is a remote data science sports job?

A remote data science sports job involves analyzing sports-related data to extract insights, build predictive models, and support decision-making, all while working from a location outside of a traditional office, typically from home. Professionals in this role use statistical methods, programming, and machine learning to evaluate player performance, game strategies, or fan engagement. Their work helps sports teams, leagues, media companies, and betting firms make evidence-based decisions. Remote positions offer flexibility and often require strong communication skills to collaborate with teams virtually. The demand for these roles is growing as the sports industry increasingly relies on data-driven strategies.

What are the key skills and qualifications needed to thrive as a remote data science sports professional?

To thrive as a Remote Data Science Sports professional, you need a strong background in statistics, data analysis, and sports knowledge, often supported by a degree in mathematics, statistics, computer science, or a related field. Familiarity with programming languages such as Python or R, proficiency in data visualization tools, and experience with machine learning frameworks are typically required. Excellent problem-solving abilities, communication skills, and self-motivation are crucial soft skills for collaborating remotely and translating complex data into actionable insights. These skills ensure accurate sports data modeling, effective remote teamwork, and valuable contributions to decision-making in sports organizations.

How do remote data science professionals in the sports industry typically collaborate with coaches and analysts to turn data insights into actionable strategies?

Remote data science professionals in the sports industry often work closely with coaches, analysts, and other stakeholders through regular virtual meetings and collaborative platforms. They translate complex data findings into intuitive visualizations and reports, making it easier for non-technical team members to understand and apply insights. Communication and responsiveness are key, as data scientists may need to quickly adjust analyses based on feedback or new priorities from the sports staff. Building strong relationships and maintaining clear channels of communication help ensure that data-driven recommendations are effectively integrated into training, game strategies, and player development.

What is the difference between Remote Data Science Sports vs Remote Data Analysis Sports?

AspectRemote Data Science SportsRemote Data Analysis Sports
Required CredentialsBachelor's/Master's in Data Science, Statistics, or related fields; programming skills in Python/RBachelor's in Data Analysis, Statistics, or related fields; proficiency in Excel, SQL, and visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves modeling and machine learningData interpretation, reporting, and visualization, often in business contexts
Employer & Industry UsageTech companies, sports analytics firms, media outletsSports teams, media companies, sports analytics agencies

Remote Data Science Sports involves advanced modeling, machine learning, and statistical analysis, requiring higher technical credentials. Remote Data Analysis Sports focuses on interpreting data, creating reports, and visualizations. Both roles are common in sports industry analytics but differ in complexity and technical depth.

Can data science be used in sports?

Data science is widely used in sports to analyze player performance, optimize strategies, and improve team decision-making. Sports data analysts and data scientists utilize tools like machine learning, statistical models, and data visualization to gain insights and enhance athletic outcomes.

Do sports teams hire remote data scientists?

Some sports teams and organizations hire remote data scientists to analyze player performance, game strategies, and fan engagement using data analytics tools. These roles often require skills in statistical modeling, machine learning, and programming languages like Python or R, and may involve collaboration with on-site staff or remote work environments.

How much do remote data science sports make?

Remote data science roles in sports typically have salaries ranging from $70,000 to $130,000 annually, depending on experience, education, and the complexity of projects. Senior positions or those requiring specialized skills in machine learning or sports analytics can earn higher compensation, often exceeding $150,000. These roles often require proficiency in programming languages like Python or R and familiarity with sports data sources and analytics tools.
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Remote Data Science Sports jobs near you
Infographic showing various Remote Data Science Sports job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution.

Data Scientist

San Francisco, CA • Remote

Swish Analytics
Spectator Sports • 1 - 10 employees
Learn more about Swish Analyticsexternal

$150K/yr

Full-time

Re-posted 20 days ago

Key responsibilities

  • Develop infrastructure for trader automation and system performance tracking

  • Analyze live-streaming data and turn it into actionable decisions

  • Design and set up tests to detect unexpected changes to models resulting from manual interactions

Job description

Company Description

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.

Job Description

You'll be joining a team that is working to develop the infrastructure for a system to optimize our simulation outputs based on a variety of external and internal signals. This role will work at the intersection of data science, trading, and data engineering to help develop and maintain trader automation algorithms that will react faster to signals as well as improve model accuracy. This position is remote from the USA or Canada.

Duties:

  • Develop infrastructure for trader automation and system performance tracking

  • Develop high-performance and low-latency products to react to external and internal signals

  • Analysis of live-streaming data and turning it into actionable decisions

  • Design and set up tests to detect unexpected changes to our models resulting from manual interactions.

  • Use extensive experience to build, test, debug, and deploy production-grade components

  • Ideate, develop, and improve machine learning and statistical models that drive Swish's core algorithms, growing into top-level simulation output modeling as we expand to new sports.

  • Develop contextualized feature sets that draw on sports-specific domain knowledge.

  • Contribute across all stages of model development — from proof-of-concept and beta testing to partnering with data engineering and product teams to deploy new models.

  • Constantly improve model performance using insights from rigorous experimentation.

  • Assess model performance, identify weaknesses, and use those findings to direct development efforts.

  • Document your work and present it clearly to technical and non-technical partners.

Requirements

  • Bachelor's in Data Science, Statistics, Computer Science, Applied Math, or a related technical field; Master's strongly preferred.

  • 4+ years developing and delivering effective machine learning and/or statistical models to serve real business needs in sports analytics or sports betting.

  • Experience in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, and Markov Chain Monte Carlo methods.

  • Excellent analytical and problem-solving ability, and a demonstrated drive to learn quickly in unfamiliar territory.

  • Experience with Python and relational SQL.

  • Strong foundation with source control (GitHub) and related CI/CD processes.

  • Strong foundation working in AWS environments.

  • Ideal candidates will have experience with Kafka, Docker, and Kubernetes

  • Ability to partner across teams on complex, ambiguous problems and communicate clearly with technical and non-technical audiences.

Base salary: Starting at $150,000 - DOE

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to those outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks.