Jobs at Swish Analytics

Own and lead backend products from architecture through production operation. Design high-traffic APIs and microservices, improve application and database performance, build real-time distributed data services, and enhance reliability, observability, automation, Kubernetes workloads, and cloud efficiency. Review code, establish engineering standards, coordinate dependencies, communicate tradeoffs, and drive ambiguous technical initiatives through rollout.

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 sportsbook risk and profitability through positive expected value decisions, maintain accurate depth charts, adjust prematch and in-play markets, research verified sports news, and analyze betting trends to drive quantifiable trading actions. Requires at least three years of online sportsbook in-game experience, strong odds and statistical knowledge, familiarity with major North American sports, and flexible availability for nights, weekends, and holidays.

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.

Manage client risk and betting margins, maintain depth charts, monitor news and betting markets, and convert betting data into quantitative trading actions for prematch and in-play markets.

Lead end-to-end research and production pipelines for systematic trading strategies. Conduct alpha research using statistical and ML techniques on high-frequency tick and order-book data, build Monte Carlo simulations and real-time risk monitoring, design position-sizing and stress-testing frameworks, mentor junior researchers, and collaborate with trading and infrastructure teams to deploy production-grade models and risk controls.

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

Develop, validate, and deploy machine learning and statistical models for soccer betting products. Create soccer-specific features, run offline and online experiments, collaborate with data engineering and product teams, follow software engineering best practices, document work, and present findings to technical and non-technical stakeholders.