Teams at Swish Analytics

Investigate production incidents using raw real-time data, identify root causes, and recommend resolutions. Build statistical system-health metrics, descriptive reports, and repeatable investigation tooling. Analyze event-driven systems during live sports events, work independently on ambiguous problems, and collaborate with data science, engineering, and trading teams. Develop and maintain production software using Python and Rust.

Manage NHL and multi-sport betting risk, pricing, exposure, and live in-play markets. Monitor depth charts, news, betting trends, line movement, and game events to make data-driven trading decisions. Build and test betting models, calculate odds and expected value, and improve profitability. Requires flexible scheduling, including nights, weekends, and holidays, with collaboration across trading and data science teams.

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.