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

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

Soccer Data Scientist

Key skills