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Remote Sports Modeling Jobs (NOW HIRING)

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Data Science Manager - Sports Pricing

Atlanta, GA · On-site +1

$160K - $190K/yr

Oversee development and deployment of pricing models across major sports and esports (NBA, NFL, CS2 ... S. and are willing to consider remote candidates. #LI-Remote Working at PrizePicks: The typical ...

Breakway Hoops

2025-2026 Multi-Sports Coach

Manhattan, NY · On-site +1

$18 - $30/hr

Formulate and model moral values, pride of accomplishment, acceptable social behavior, self ... Collect, distribute, and maintain the remote and athletic equipment during the season * Instill in ...

Crypto.com

Quant Trader (Sports Event Market Making)

$120K - $180K/yr

Monitor 24/7 market activity and update trading models with the latest data in collaboration with ... Flexi-work hour and hybrid or remote set-up Aspire career alternatives through us - our internal ...

Underdog

Senior Product Manager, Sports Predictions

OR · Remote

$160K - $240K/yr

... probability modeling and prediction market mechanics, not just surface-level product decisions ... LI-REMOTE We're a remote-first company and value in-person connection. That said, we expect ...

Swish Analytics

Tennis Data Scientist

San Francisco, CA · On-site +1

$135K - $190K/yr

This position is remote from the USA. Duties: * Ideate, develop and improve machine learning and statistical models that drive Swish's core algorithms for producing state-of-the-art sports betting ...

Texas Sports Academy

Virtual Guide (Remote - USA)

As a Virtual Guide with Texas Sports Academy Online, you play a key role in helping student-athletes navigate our two-hour-a-day academic model and integrated sports pathway. You will support ...

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Remote Sports Modeling information

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How much do remote sports modeling jobs pay per hour?

As of Jun 27, 2026, the average hourly pay for remote sports modeling in the United States is $40.33, according to ZipRecruiter salary data. Most workers in this role earn between $31.25 and $43.51 per hour, depending on experience, location, and employer.

How does a Remote Sports Modeling professional typically collaborate with data scientists and analysts to improve predictive models?

In a Remote Sports Modeling role, collaboration with data scientists and analysts is essential for refining and validating predictive models. This often involves regular virtual meetings to review model performance, share insights from statistical analyses, and troubleshoot data discrepancies. Professionals in this field are expected to communicate complex modeling concepts clearly, incorporate feedback, and work iteratively to enhance model accuracy. Effective teamwork leads to more robust predictions and can directly impact decision-making for clients or organizations.

What is the difference between Remote Sports Modeling vs Remote Sports Commentating?

AspectRemote Sports ModelingRemote Sports Commentating
Required CredentialsModeling experience, portfolio, sometimes sports knowledgeSports knowledge, broadcasting skills, sometimes journalism background
Work EnvironmentHome studio, online platformsHome studio, live or recorded broadcasts
Employer & Industry UsageSports brands, marketing agencies, media companiesBroadcast networks, online streaming services, sports media

Remote Sports Modeling involves creating visual content or promotional images related to sports, focusing on modeling skills and sports familiarity. Remote Sports Commentating entails providing live or recorded commentary on sporting events, requiring sports knowledge and broadcasting skills. While both roles are remote and industry-related, they serve different functions: one is visual promotion, the other is live narration.

What is remote sports modeling?

Remote sports modeling involves using statistical techniques, data analysis, and predictive algorithms to forecast outcomes in sporting events, all while working from a remote location. Professionals in this field analyze historical data, player statistics, and other relevant variables to build models that predict scores, player performance, or game results. These models are often used by sportsbooks, teams, or analysts for decision-making and strategy development. The remote aspect allows for flexibility, enabling professionals to work from anywhere with internet access.

What are the key skills and qualifications needed to thrive as a Remote Sports Modeler, and why are they important?

To thrive as a Remote Sports Modeler, you need a strong background in statistics, data analysis, and sports knowledge, often supported by a relevant degree in mathematics, statistics, or a related field. Proficiency with programming languages like Python or R, experience with statistical software, and familiarity with sports data feeds and modeling tools are commonly required. Strong problem-solving, attention to detail, and clear communication skills help you interpret data and explain complex findings to stakeholders. These skills ensure accurate predictive models, effective collaboration, and valuable insights for sports analytics or betting organizations.
More about Remote Sports Modeling jobs
What cities are hiring for Remote Sports Modeling jobs? Cities with the most Remote Sports Modeling job openings:
What are the most commonly searched types of Sports Modeling jobs? The most popular types of Sports Modeling jobs are:
What states have the most Remote Sports Modeling jobs? States with the most job openings for Remote Sports Modeling jobs include:
What job categories do people searching Remote Sports Modeling jobs look for? The top searched job categories for Remote Sports Modeling jobs are:
Remote Sports Modeling jobs near you
Infographic showing various Remote Sports Modeling job openings in the United States as of June 2026, with employment types broken down into 78% Full Time, 19% Part Time, and 3% Contract. Highlights an 37% Physical, 3% Hybrid, and 60% Remote job distribution, with an average salary of $83,896 per year, or $40.3 per hour.
Sports Data Analyst

Sports Data Analyst

Swish Analytics

San Francisco, CA • On-site, Remote

Full-time

Posted yesterday

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.
Department Data Science Role NFL Team Locations San Francisco, CA - Remote Remote status Fully Remote

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