Basketball Data Scientist
San Francisco, CA - Remote
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.
Job Description
Swish Analytics is hiring Basketball Data Scientists to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products. We're hiring a Data Scientist to support our Sports Data Models
Duties:
- 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.
Requirements:
- Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area; PhD highly preferred
- Demonstrated experience developing models at production scale for baseball or sports betting
- Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
- Minimum of 1 year of demonstrated experience (8+ for higher level) developing and delivering effective machine learning and/or statistical models to serve business needs
- 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 …
This job isn't fresh anymore!
Search Fresh JobsJob Profile
- Adhere to software engineering best practices
- Analyze model performance
- Contribute to shared code repositories
- Develop machine learning models
- Document modeling work
- Improve statistical models
AWS Bayesian Statistics CI/CD Computer Science Data & Analytics Data Science GitHub Inferential Statistics Machine Learning Markov chain monte carlo Mathematics Probability Theory Python Software Engineering Sports betting SQL Statistical Statistical models
Experience1-3 years
EducationComputer Science Data Analytics Data Science Master's degree Ph.D. Statistics
TimezonesAmerica/Anchorage America/Chicago America/Denver America/Los_Angeles America/New_York Pacific/Honolulu UTC-10 UTC-5 UTC-6 UTC-7 UTC-8 UTC-9