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Baseball Data Science Jobs (NOW HIRING)

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 ...

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 ...

Tread Athletics

Data Support Internship

A drive to contribute to advancing the application of data science in baseball and athletic performance Applicants must be legally authorized to work or participate in an internship in the United ...

Creative Artists Agency (CAA)

AI - Data Engineer

Nashville, TN · On-site

$110K - $132K/yr

... baseball, basketball, hockey, soccer, in addition to coaches, on-air broadcasters, and sports ... Work closely with cross functional team to align data science initiatives with business priorities

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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 Aug 10, 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 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.

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 August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.

Senior Data Scientist

Ex Parte, Inc

Bethesda, MD • On-site, Remote

Full-time

Re-posted 29 days ago

Job description

Company Description
Ex Parte provides our customers with the data and insight to make smart and informed decisions on the most important legal issues facing their organizations.
We are is looking for talented, enthusiastic senior data engineers who share our passion for big data, AI, and machine learning and are excited by seemingly-impossible challenges. As an early employee, you must be amazingly entrepreneurial and thrive in a fast-paced environment where the solutions aren't predefined.
Every year, corporations spend more than $250B on litigation in the United States alone. And yet, critical decisions such as whether to litigate or settle, or where to file suit or which attorney to hire, are all made the same way they were 100 years ago.
We are applying artificial intelligence, machine learning, and natural language processing to provide our customers with the insight they need to make highly informed decisions and gain a winning advantage. Think of it like Moneyball, but for a market more than 20x the size of Major League Baseball.
Job Description
  • Drive technical roadmap to extend risk monitoring across identified threat surfaces.
  • Develop/experiment/ship state-of-the-art prediction models
  • Use excellent data science practices to iteratively produce high performing models
  • Create immediate impact through sound and practical deliveries of risk monitors
  • Work with engineering colleagues to convey findings through data visualizations
  • Measure, tune and refine existing algorithms to incrementally improve performance
  • Analyze new and existing data after extracting. transforming and combining it in novel ways
  • Convey needs to engineering and operations teams to ensure healthy feedback loops
  • Attract and onboard new talent while preserving and enhancing existing culture
  • Build strong partnerships and collaborate with other teams across the enterprise
  • Demonstrate sense ownership and personal accountability for your team's work

Qualifications
Basic Qualifications:
  • 5+ years applied data science experience, including 3 years of advanced analytics experience focused on enterprise-specific problem solving
  • 2+ years management, mentoring, or other closely related team or people leadership experience
  • Experience in machine learning (supervised, semi-supervised or unsupervised learning)
  • Strong communication, delivery management, and leadership skills

Preferred Qualifications
  • A bachelor's degree, MSc or Ph.D. in Statistics, Data Science, Artificial Intelligence, or equivalent alternative education or experience
  • Applied experience with SQL, also Python or R or Scala, and modern data science tools/packages e.g. PyTorch, Transformers, TensorFlow, scikit-learn
  • Applied experience with Databricks and/or Azure ML
  • Strong coding abilities in one or more scripting languages like Python or SQL
  • Understanding of compliance, security, and risk domains along with associated patterns and data elements
  • Understanding of product and services activation, use, and transaction models and data
  • Understanding of statistical analysis and machine learning tools and practices
  • Understanding of Cloud-centric data processing and visualization approaches including SQL and NoSQL databases with exposure to Azure SQL, Azure Cosmos DB, Data Factory, Synapse, Azure Data Lake, etc
  • Familiarity with Agile software delivery including application lifecycle mgmt (SAFe, Azure DevOps/VSTS, Git)

Additional Information
All your information will be kept confidential according to EEO guidelines.

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