Jobs at Swish Analytics

Develop, validate, and deploy production-scale machine learning and statistical models for NFL sports betting products. Create domain-specific features, run offline/online experiments to improve performance, collaborate with engineering and product teams, follow software engineering best practices, and communicate results to technical and non-technical stakeholders.

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.

Develop, validate, and deploy soccer-focused machine learning and statistical models for sports betting; create contextual features, run offline/online experiments, collaborate with engineering and product teams, document work, and present results to technical and non-technical stakeholders.

Manage client risk and maximize margins by making +EV pre-match and in-play decisions. Monitor depth charts, research verified news, analyze betting trends, and convert data into quantifiable trading actions. Work flexible hours and collaborate with cross-functional teams to improve processes and profitability.

Manage client risk and margins for sports-betting products, maintain depth chart accuracy pre-match and in-play, source and assess breaking news, and analyze betting trends to generate data-driven trading actions.

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.

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, 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.