Swish Analytics is looking to solve the challenge of oddsmaking in sports betting by building predictive sports analytics data products using engineering, mathematics, and sports betting expertise
Requirements
- 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
- Demonstrated experience developing models at production scale for tennis or sports betting
- Minimum of 4+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting
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
Other
- Bachelors degree in Data Analytics, Data Science, Computer Science or related technical subject area; Masters highly preferred
- Proven track record of strong leadership skills
- Excellent communication skills to both technical and non-technical audiences
- Ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions
