Soccer Data Scientist

September 7

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Swish Analytics

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Intelligent U.S. Sports Betting Solutions

Sports Analytics • sports betting • gambling

11 - 50

💰 $6.9M Series B on 2019-05

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. • 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 specific domain knowledge in soccer. • Contribute to all stages of model development. • 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. • 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 • Demonstrated experience developing models at production scale for soccer or sports betting • Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods • Minimum of 3+ 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