Remote Data Scientist

Summary

Join our team at Swish Analytics as a Tennis Data Scientist and contribute to developing core components of Swish's data products. As a key member of the data science team, you will ideate, develop, and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products.

Requirements

• Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area
• Minimum of 1 year of demonstrated experience developing models at production scale for tennis or sportsbook at scale
• Minimum of 3 years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs
• Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
• 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

Responsibilities

• 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