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Sports Data Python Jobs (NOW HIRING)

Swish Analytics

Sports Data Analyst

San Francisco, CA · On-site +1

Source origins of data inaccuracies through data pipeline dependencies and python code base ... Deep knowledge of a second sport including football, basketball, baseball, hockey or tennis

SumerSports LLC

Data Engineer

$117K - $140K/yr

You'll collaborate closely with our MLOps , LLMOps , and Sports Data teams to ensure seamless ... Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads.

RightsHelper

Senior Data Engineer

$150K - $175K/yr

Develop data pipelines in Python using modern best practices * Improve pipeline observability ... Experience with analytics or sports data * Experience working in startup or small team environments ...

Kalshi

Sports Operations Engineer

New York, NY · On-site

$76K - $102K/yr

Sports Operations Engineer - Kalshi Kalshi is defining a new category Kalshi has defined a new ... Strong proficiency in Python and JavaScript with experience building automation scripts, data ...

Kalshi

Sports Operations Engineer

New York, NY · On-site

$76K - $102K/yr

Sports Operations Engineer - Kalshi Kalshi is defining a new category Kalshi has defined a new ... Strong proficiency in Python and JavaScript with experience building automation scripts, data ...

AEG

Sports Scientist

San Antonio, TX · On-site

Proficiency in R (preferred), Python, and SQL for data processing and visualization. * Experience ... The Sports Scientist must be able to lift and transport equipment weighing up to 50 pounds and work ...

AEG

Sports Trading Analyst

San Francisco, CA · On-site

$70K - $120K/yr

Experience using Python for quantitative research, exploratory data analysis, prototyping, or model ... sports trading operation. The successful candidate will help shape process, tooling, and decision ...

SumerSports LLC

AI Application Engineer

... Sports Data teams to ship high-quality, domain-aware, and trustworthy AI experiences ... Proficiency in Python and TypeScript/JavaScript (Node.js or React). * Hands-on experience with LLM ...

Swish Analytics

Senior Software Engineer

San Francisco, CA · On-site +1

$150K - $190K/yr

... sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering ... Python * 1+ years of production experience with databases both relational and non-relational

FanDuel

Risk Data Science, Director

Jersey City, NJ

The role requires a strong knowledge of sports betting proven management experience. You will be a ... Python, SQL, and modern data science tooling. * Strong stakeholder management skills with the ...

AEG

Data Science Manager - Sports Pricing

Atlanta, GA · On-site

$160K - $190K/yr

... data scientists. * Oversee development and deployment of pricing models across major sports and ... Proficiency in SQL and Python or R; experience with simulation-based modeling and statistical ...

PrizePicks

Data Science Manager - Sports Pricing

Atlanta, GA · On-site +1

$160K - $190K/yr

... data scientists. * Oversee development and deployment of pricing models across major sports and ... Proficiency in SQL and Python or R; experience with simulation-based modeling and statistical ...

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How much do sports data python jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for sports data python in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Sports Data Python Analyst, and why are they important?

To excel as a Sports Data Python Analyst, you need strong programming skills in Python, expertise in data analysis, and a background in statistics or a related field. Familiarity with tools like Pandas, NumPy, SQL, and data visualization libraries such as Matplotlib or Seaborn, as well as experience with sports analytics platforms, is typically required. Analytical thinking, attention to detail, and effective communication are crucial soft skills for interpreting data and presenting actionable insights. These competencies enable analysts to extract meaningful patterns from complex sports data, supporting informed decision-making for teams, coaches, or organizations.

What is the difference between Sports Data Python vs Sports Data Analyst?

AspectSports Data PythonSports Data Analyst
Required SkillsPython programming, data analysis, scriptingData analysis, statistical skills, reporting
Work EnvironmentData science teams, tech companies, sports analytics firmsSports teams, media outlets, analytics departments
Common CertificationsPython certifications, data analysis coursesSports management, analytics certifications

Sports Data Python focuses on programming and scripting to analyze sports data, often requiring coding skills and technical expertise. In contrast, Sports Data Analysts interpret data, generate reports, and provide insights for decision-making. Both roles are vital in sports analytics but differ in technical depth and daily tasks.

What is a Sports Data Python professional?

A Sports Data Python professional specializes in using the Python programming language to collect, analyze, and interpret sports data. They work with large datasets from sources like player statistics, game results, and sensor data to generate insights for teams, coaches, analysts, or fans. Their work often involves data cleaning, statistical analysis, visualization, and sometimes developing predictive models to forecast outcomes or player performance. These professionals are essential in the growing field of sports analytics, helping organizations make data-driven decisions.

What are some typical challenges faced by Sports Data Python professionals when working with real-time data feeds?

Sports Data Python professionals often deal with the complexity of ingesting and processing real-time data from multiple sources, which can present challenges such as handling inconsistent data formats, ensuring minimal latency, and maintaining data accuracy. They must also develop robust error-handling mechanisms to account for feed interruptions or anomalies. Close collaboration with data engineers, analysts, and sometimes product teams is common to ensure seamless integration of live data into analytics platforms or applications.
Infographic showing various Sports Data Python job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $121,932 per year, or $58.6 per hour.
Sports Data Analyst

Sports Data Analyst

Swish Analytics

San Francisco, CA

Full-time

Posted 11 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.