Teams at Swish Analytics

Design and build low-latency, event-driven trading systems for sports betting exchanges: real-time fair-value decisioning, multi-venue order execution, position and risk management, data pipelines, reconciliation, and resilient integrations with exchange APIs.

Develop, test, and deploy production-scale machine learning and statistical models for tennis sports betting. Create contextualized feature sets, run offline and online experiments to improve performance, collaborate with engineering and product teams, follow software engineering best practices, document work, and present results to technical and non-technical stakeholders.

Monitor and validate sports data pipelines, detect and trace inaccuracies, define validation tests, research roster and participation data, support feature development and model analysis with Data Scientists, document findings, and maintain familiarity with databases and metadata.