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Sports Analytics Machine Learning Jobs (NOW HIRING)

Swish Analytics

Machine Learning Engineer

San Francisco, CA · On-site +1

$160K/yr

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of ... The Data Science team is hiring an experienced Machine Learning Engineer with a background building ...

Swish Analytics

Tennis Data Scientist

San Francisco, CA · On-site +1

$135K - $190K/yr

... Tennis or sports betting for 2+ years * Expertise in Probability Theory, Machine Learning ... 190,000 Swish Analytics is an Equal Opportunity Employer. All candidates who meet the ...

Swish Analytics

NFL Data Scientist

San Francisco, CA · On-site +1

$140K/yr

Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the ... CFB, or sports betting for 2+ years * Expertise in Probability Theory, Machine Learning ...

Swish Analytics

NFL Data Scientist

San Francisco, CA

$140K/yr

Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the ... CFB, or sports betting for 2+ years * Expertise in Probability Theory, Machine Learning ...

PTP

Machine Learning Engineer - REMOTE

Austin, TX · On-site +1

Analyze and extract key insights from rich stores of customer data * Research and implement ML ... Machine learning (ML) algorithms * Predictive modeling and analysis * Data visualization software ...

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Sports Analytics Machine Learning information

What is sports analytics machine learning?

Sports analytics machine learning is the application of data science and machine learning techniques to analyze sports data, such as player statistics, game outcomes, and biometric information. Professionals in this field develop models to identify patterns, predict player performance, optimize team strategies, and gain competitive advantages. This work involves collecting large datasets, cleaning and processing data, and using algorithms to extract actionable insights that can benefit teams, coaches, and athletes. Sports analytics with machine learning is increasingly used in professional sports to inform decisions about training, recruitment, and game tactics.

How do Sports Analytics Machine Learning professionals typically collaborate with coaches and athletes to impact game strategy?

Sports Analytics Machine Learning professionals often work closely with coaches and athletes by translating complex data insights into practical recommendations. They attend strategy meetings, present findings through visualizations, and help interpret trends that can influence training, player selection, and in-game tactics. Effective communication is key, as these professionals must bridge the gap between technical analyses and real-world sports applications. This collaborative environment not only enhances team performance but also provides opportunities to see the direct impact of your work on the field.

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

To thrive as a Sports Analytics Machine Learning Specialist, you need a strong background in statistics, data analysis, programming (typically in Python or R), and an understanding of machine learning algorithms, often supported by a degree in data science, statistics, or a related field. Familiarity with data visualization tools, sports databases, and machine learning frameworks like TensorFlow or scikit-learn is essential, along with experience using SQL and data pipelines. Strong problem-solving, communication, and collaboration skills help translate complex data findings into actionable insights for coaches, players, and stakeholders. These skills are crucial for extracting meaningful patterns from vast sports datasets and driving performance improvements or strategic decisions within sports organizations.
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Sports Analytics Machine Learning jobs near you
Infographic showing various Sports Analytics Machine Learning job openings in the United States as of May 2026, with employment types broken down into 80% Full Time, 13% Part Time, and 7% Temporary. Highlights an 93% In-person, and 7% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Swish Analytics

San Francisco, CA • Remote

$160K/yr

Full-time

Posted 21 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.