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# NFL Data Scientist

- **Company:** Swish Analytics Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $140,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Information Engineering, Github, Python (Programming Language), Machine Learning, Monte Carlo Methods, Standard Sql, Software Construction, Beta Testing, Information Technology, Data Analytics, Markov, Software Version Control
- **Published:** September 11, 2026
- **Apply:** https://www.thejobnetwork.com/job/0c8d06f0-709f-4411-b483-32308226bc96/nfl-data-scientist

## About the Role

* Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area
 * Demonstrated experience developing models at production scale for NFL, CFB, or sports betting for 2+ years
 * Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
 * 5+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting
 * Experience with relational SQL & Python
 * Experience with source control tools such as GitHub and related CI/CD processes
 * Experience working in AWS environments etc
 * Proven track record of strong leadership skills. Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions
 * Excellent communication skills to both technical and non-technical audiences

## Description

* Ideate, develop and improve machine learning and statistical models that drive Swish's core algorithms for producing state-of-the-art sports betting products.
 * Develop contextualized feature sets using sports specific domain knowledge.
 * Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models.
 * Strive to constantly improve model performance using insights from rigorous offline and online experimentation.
 * Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts.
 * Adhere to software engineering best practices and contribute to shared code repositories.
 * Document modeling work and present to stakeholders and other technical and non-technical partners.


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