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Baseball Data Science Jobs (NOW HIRING)

Ex Parte, Inc

Senior Data Scientist

Bethesda, MD · On-site

... League Baseball. * Drive technical roadmap to extend risk monitoring across identified threat surfaces. * Develop/experiment/ship state-of-the-artprediction models * Use excellent data science ...

Ex Parte, Inc

Senior Data Scientist

Bethesda, MD · On-site +1

... League Baseball. * Drive technical roadmap to extend risk monitoring across identified threat surfaces. * Develop/experiment/ship state-of-the-art prediction models * Use excellent data science ...

Baltimore Orioles

Data Scientist Fellow

Baltimore, MD · On-site

$20/hr

Baseball Analytics Job Type: Part-time, seasonal, hourly Job Summary This position is responsible ... Science, or equivalent. * Experience with SQL. * Experience with R or Python and pragmatic ...

The New York Mets

Data Engineer

Queens, NY · On-site

$90K/yr

Prior experience in or knowledge of baseball is a plus but is not required. Essential Duties ... Onboard new datasets and technologies for use by analysts, data scientists, players and other ...

New York Mets Organization

Data Engineer

Corona, NY · On-site

$120K - $144K/yr

Prior experience in or knowledge of baseball is a plus but is notrequired. Essential Duties ... Onboard new datasets and technologies for use by analysts, data scientists,playersand other ...

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Baseball Data Science information

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$41.5K

$142.5K

$201K

How much do baseball data science jobs pay per year?

As of Jun 18, 2026, the average yearly pay for baseball data science in the United States is $142,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $166,500.00 per year, depending on experience, location, and employer.

How do baseball data scientists typically collaborate with coaches and players to translate analytics into on-field improvements?

Baseball data scientists often work closely with coaches and players by presenting data-driven insights in accessible ways, such as visualizations or concise reports. They help translate complex analytics into actionable strategies, like adjusting swing mechanics or defensive positioning. Regular meetings and open communication are key, as data scientists must ensure their recommendations align with team goals and player capabilities. This collaborative approach not only bridges the gap between data and performance but also fosters a culture of continuous improvement.

What is the difference between Baseball Data Science vs Baseball Analytics?

AspectBaseball Data ScienceBaseball Analytics
Required CredentialsDegree in Data Science, Statistics, or related fieldDegree in Sports Management, Analytics, or related field
Work EnvironmentData-driven teams, sports organizations, research labsTeam analysis departments, sports teams, consulting firms
Employer & Industry UsageMajor league teams, sports analytics companies, research institutionsMajor league teams, sports media, consulting firms

Baseball Data Science focuses on advanced statistical modeling, machine learning, and data engineering to uncover insights from complex datasets. Baseball Analytics often emphasizes performance metrics, game strategy, and player evaluation using statistical tools. While both roles overlap, Data Science tends to involve more technical data manipulation, whereas Analytics centers on applying insights to game strategies and player decisions.

What is baseball data science?

Baseball data science is the application of statistical analysis, machine learning, and data management techniques to baseball data to gain insights, improve player performance, and inform team strategies. Data scientists in baseball analyze large datasets such as player statistics, pitch tracking, and game outcomes to uncover patterns and make predictions. Their work supports coaching decisions, scouting, player health monitoring, and front office operations. Baseball data science has become increasingly important with the rise of advanced metrics and technologies like Statcast.

How to become an MLB data analyst?

To become an MLB data analyst, candidates typically need a strong background in statistics, data analysis, or computer science, often with a bachelor's degree in a related field. Proficiency in programming languages such as Python or R, experience with sports data, and knowledge of baseball metrics are important. Gaining experience through internships or projects and understanding baseball analytics tools like Statcast or TrackMan can improve job prospects.

How is data science used in baseball?

In baseball data science involves analyzing player and game data to improve team strategies, player performance, and scouting. Data scientists use statistical models, machine learning, and visualization tools to identify patterns and make data-driven decisions that enhance team success.

How much do MLB statisticians make?

MLB statisticians typically earn between $50,000 and $100,000 annually, depending on experience, role, and team size. Many work as part-time consultants or analysts, often utilizing data analysis tools and statistical software to support team strategies and player evaluations.

How much do MLB data scientists make?

MLB data scientists typically earn between $70,000 and $120,000 annually, depending on experience, education, and the level of responsibility. Salaries can vary based on the organization, location, and whether they have specialized skills in statistics, programming, or sports analytics tools.

What are the key skills and qualifications needed to thrive as a Baseball Data Scientist, and why are they important?

To thrive as a Baseball Data Scientist, you need a strong background in statistics, data analysis, and computer science, often supported by a degree in a quantitative field. Familiarity with programming languages like Python or R, experience with SQL databases, and proficiency in data visualization tools are typically required. Strong communication, problem-solving abilities, and a passion for baseball analytics make candidates stand out. These skills are crucial for extracting actionable insights from complex data, supporting decision-making, and driving competitive advantage in baseball operations.
More about Baseball Data Science jobs
What cities are hiring for Baseball Data Science jobs? Cities with the most Baseball Data Science job openings:
What are the most commonly searched types of Baseball Data Science jobs? The most popular types of Baseball Data Science jobs are:
What states have the most Baseball Data Science jobs? States with the most job openings for Baseball Data Science jobs include:
What job categories do people searching Baseball Data Science jobs look for? The top searched job categories for Baseball Data Science jobs are:
Baseball Data Science jobs near you
Infographic showing various Baseball Data Science job openings in the United States as of June 2026, with employment types broken down into 88% Full Time, and 12% Part Time. Highlights an 99% Physical, and 1% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.
Sports Data Analyst

Sports Data Analyst

Swish Analytics

San Francisco, CA

Full-time

Posted 22 days ago

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.

Duties:

  • Work closely with Data Scientists and Engineers to diagnose and treat data pipeline integrity issues

  • Detect data inaccuracies such as missing, out of range or otherwise incorrect on-field data

  • Source origins of data inaccuracies through data pipeline dependencies and python code base

  • Define data validation tests to flag future game errors

  • Research accurate roster active statuses, primary positions and game participation

  • Validate data changes after logic updates

  • Production model feature deep dives to explain project market lines

  • Clearly document findings

  • Develop intimate familiarity with existing databases and construct metadata references

  • With guidance, support lead Data Scientists in feature development and model analysis

Requirements:

  • Bachelor's Degree in Computer Science, Data Science or similar major

  • Minimum of 1 year of experience in football data analysis

  • Deep knowledge of football, basketball or baseball; including roster compositions of professional and college teams, general gameplay strategies, and typical in-game scenarios

  • Data Extraction, Wrangling and Analysis in Python

  • Strong SQL querying skills

  • Attention to detail

Preferred:

  • Strong Python data management programming skills

  • Data Visualization experience with a user application like Streamlit

  • Deep knowledge of a second sport including football, basketball, baseball, hockey or tennis

  • Exposure to the data science process and tech stack

  • Anomaly Detection Techniques

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.