Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

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 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.

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.

Analyze factor usage and market data to detect simulation impacts, design tests, build and improve machine learning and statistical models for sports betting, develop sports-specific features, run offline/online experiments, and collaborate with engineering and product teams to deploy models.

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.

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.

Build and maintain low-latency, real-time analytics and ETL pipelines for sports betting products. Support production systems during live events, integrate complex datasets, develop predictive analytics and enterprise APIs, and contribute to automated sports data delivery frameworks.

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.