About the role
About the Company Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. Responsibilities Design, prototype, implement, evaluate, and optimize systems to generate sports datasets and predictions with high accuracy and low latency Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow Build, test, deploy and maintain production systems Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages Support maintenance and optimization of cloud-native EDW and ETL solutions Maintain and promote best practices for software development, including deployment process, documentation, and coding standards Participate in development of database structures that fit into the overall architecture of Swish systems Requirements Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area 5+ years of experience developing and delivering clean and efficient production code Proven background in quantitative analytics, trading, or engineering Demonstrated experience developing data science modeling systems and infrastructure at scale Experience with Python and modern machine learning frameworks Proficient in SQL and experience with MySQL Preferred Qualifications Background and/or interest in Rust