Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. Responsibilities include designing, prototyping, implementing, evaluating, and optimizing systems to generate sports datasets and predictions with high accuracy and low latency; evaluating internal modeling frameworks and tools; building, testing, deploying and maintaining production systems; working 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; supporting maintenance and optimization of cloud-native EDW and ETL solutions; maintaining and promoting best practices for software development; applying large scale data processing techniques to develop scalable and innovative sports betting products; participating in development of database structures that fit into the overall architecture of Swish systems. Qualifications include a 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; experience developing data science modeling systems and infrastructure at scale; experience with Python and modern machine learning frameworks; proficiency in SQL and experience with MySQL; background and interest in Rust preferred; strong teamwork and communication skills. The position is 100% remote.
