Jobs 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.

Design and build a next-generation data analytics platform, standardize development processes, research scalable technologies, implement backend RESTful APIs and frontend features, collaborate with stakeholders and cross-functional teams, and ensure high-quality, tested software for deployment.

Senior HR generalist responsible for end-to-end onboarding/offboarding, multi-state and international payroll and benefits administration, employee relations and investigations, HR compliance and policy updates, HR metrics and reporting, and partnering with Talent Acquisition to support hiring across US and international jurisdictions.

Monitor live sports markets and support real-time price discovery, risk management, and trading operations. Calibrate prices using market signals and statistical models, identify trading signals and inefficiencies, manage exposure and liabilities, and collaborate with engineers and data teams to improve pricing infrastructure, automation, and model feedback loops.

Lead and build the PMO to enable predictable, visible cross-functional delivery. Own program delivery across Trading, Engineering, Data Science, and Data Engineering; design intake, prioritization, change management, and reporting; raise delivery maturity; and hire, coach, and grow the PMO team.

Manage client risk and maximize margins on Swish products through data-driven, +EV trading decisions. Maintain depth chart accuracy, react to prematch and in-play developments, research verified news, and analyze betting trends to produce quantifiable trading actions. Work flexible hours including nights and weekends.

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 high-performance, low-latency backend systems in Rust for a live sports analytics/trading platform. Develop, test, debug, and deploy production-grade components; improve Rust and Python codebase; build tools, investigate data pipeline inaccuracies, and perform production model feature analyses.

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.