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.

Serve as primary client contact for B2B sports betting and fantasy customers. Troubleshoot APIs and data feeds, monitor client engagement, onboard and support adoption, relay feedback to Product/Engineering/Trading/Data Science, and improve processes and documentation to drive retention and satisfaction.