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Data Science Sports Betting Jobs (NOW HIRING)

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Data Science Sports Betting information

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How much do data science sports betting jobs pay per year?

As of Aug 9, 2026, the average yearly pay for data science sports betting in the United States is $100,240.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,000.00 and $151,000.00 per year, depending on experience, location, and employer.

What is a Data Science Sports Betting?

A Data Science Sports Betting job involves using data analytics, machine learning, and statistical modeling to analyze sports events and predict betting outcomes. Professionals in this field collect and process large datasets, identify patterns, and develop predictive models to gain an edge in sports wagering. They work with odds calculation, bankroll management, and risk assessment to optimize betting strategies. This role requires expertise in programming, data manipulation, and a deep understanding of sports dynamics. It can be found in sportsbooks, betting firms, or independent consulting roles.

What does a Data Science Sports Betting professional do?

On a daily basis, a Data Science Sports Betting professional analyzes large datasets from various sports, builds and tunes predictive models, and tests new analytical algorithms to improve betting outcomes. They work closely with traders, risk managers, and software engineers to transform model outputs into actionable strategies and streamline the betting process. The role often involves monitoring real-time sports events, updating forecasts with new data, and presenting insights to both technical and non-technical stakeholders. This dynamic work environment demands continuous learning and collaboration to stay ahead of industry trends and ensure accurate, profitable recommendations.

What are the key skills and qualifications needed to thrive in data science sports betting?

To thrive as a Data Science Sports Betting professional, you need strong statistical analysis, machine learning expertise, and domain knowledge in sports betting, usually supported by a degree in data science, statistics, or a related field. Familiarity with programming languages like Python or R, experience with data visualization tools, and proficiency in platforms like SQL and cloud computing services are highly valued. Excellent problem-solving skills, adaptability, and effective communication enable professionals to explain insights and collaborate across technical and non-technical teams. These skills are vital for developing robust predictive models, interpreting complex data, and driving successful betting strategies in a fast-paced, data-driven environment.

More about Data Science Sports Betting jobs
What cities are hiring for Data Science Sports Betting jobs? Cities with the most Data Science Sports Betting job openings:
What are the most commonly searched types of Data Science Sports Betting jobs? The most popular types of Data Science Sports Betting jobs are:
What states have the most Data Science Sports Betting jobs? States with the most job openings for Data Science Sports Betting jobs include:
What job categories do people searching Data Science Sports Betting jobs look for? The top searched job categories for Data Science Sports Betting jobs are:
Data Science Sports Betting jobs near you
Infographic showing various Data Science Sports Betting job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 9% Part Time, and 9% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $100,240 per year, or $48.2 per hour.

Machine Learning Engineer

Swish Analytics

San Francisco, CA • On-site, Remote

$160K/yr

Full-time

Re-posted 22 days ago

Job 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 enterprise clients.
The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to "roll your own" and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues.
This position is 100% remote
Responsibilities:
  • Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.
  • Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.
  • Build, test, deploy and maintain production systems.
  • Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.
  • Support maintenance and optimization of cloud-native EDW and ETL solutions.
  • Maintain and promote best practices for software development, including deployment process, documentation, and coding standards.
  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
  • Use extensive experience to build, test, debug, and deploy production-grade components.
  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
  • Participate in development of database structures that fit into the overall architecture of Swish systems

Qualifications:
  • Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area
  • 5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs
  • A proven background in quantitative analytics, trading, or engineering is required for this position
  • Demonstrated experience developing data science modeling systems and infrastructure at scale
  • Experience with Python and exposure to modern machine learning frameworks
  • Proficient in SQL; experience with MySQL
  • Background and/or interest in Rust preferred
  • Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback
  • Strong communication skills when discussing technical concepts with technical and non-technical colleagues

Base salary: starting at $160,000 base plus bonus potential
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 the responsibilities outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks.
Department Engineering & Infrastructure Role Data Science Infrastructure Locations San Francisco, CA - Remote Remote status Fully Remote

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