Staff Data Scientist - Verification & Validation

Zoox is on an ambitious journey to develop a full-stack autonomous vehicle system for cities.

We are seeking a Staff Data Scientist to join a verification and validation team that evaluates safety-critical AI systems.

You will join a team of software and data engineers that leverage methods including log data analysis, simulation, and closed-course structured testing.

You'll work cross-functionally with AI software, System Design and Mission Assurance, Simulation, Sensors, and other teams to develop, execute, and iterate on validation methods and pipelines.

These pipelines evaluate safety-critical systems, are highly visible, and are an important critical path element of launching our service.

The ideal candidate brings a hybrid of statistical rigor and engineering mindset to drive clarity from ambiguity, establish new processes, and propel the team forward.

• Design Evaluation Frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.
• Conduct Robust Analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.
• Inform Strategy: Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.
• Define Metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.
• Lead the Lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.
• Scale Pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.
• MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field
• Proficiency in Python and SQL with experience in production-quality code
• Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.
• Experience with large-scale data analysis and statistical modeling
• Proficiency with Git, unit testing, and collaborative development practices
• Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines
• Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks
• Experience with designing metrics and delivering actionable insights that drive business decisions