Swish Analytics is looking to improve its sports betting products by developing more accurate and predictive real-time data models.
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 soccer or sports betting
- 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
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 specific domain knowledge in soccer.
- 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
- Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area
- 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
