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Senior Data Scientist - Verification & Validation

Zoox

Salary$209k - $285k
Before you apply

Role details

The practical picture
Employment
Full-time
Role signal brief

The role, at a glance

Extracted from the source posting · 95% confidence

Zoox seeks a Senior Data Scientist for safety-critical AI verification, working hybrid in Foster City, CA to architect statistical evaluation frameworks and build scalable validation pipelines for autonomous vehicle systems.

Compensation
USD 209,000–285,000 / year

What you need

  • MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field
  • Proficiency in Python and SQL with production-quality code experience
  • 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

What you will own

  • Design statistical evaluation frameworks for safety-critical AI systems and deliver rigorous validation evidence
  • Architect objective methodologies to assess performance and reliability of autonomous systems
  • Inform strategy through data-driven insights for development teams and executive leadership
  • Define and align performance metrics and data extraction strategies across engineering teams
  • Manage full lifecycle of evaluations from requirements capture through validation
  • Partner with engineers to build scalable data processing and simulation pipelines analyzing petabytes of driving data
Core skills
PythonSQLStatistical MethodsMachine LearningBayesian InferenceHypothesis TestingPower AnalysisSpatiotemporal ModelingApache SparkGit
Internal freshness window ends Oct 21, 2026
View source evidence
  • salary “USD 209,000 - 285,000 per year”
  • location “hybrid,Foster City, CA”
  • seniority “Senior Data Scientist”
  • must_have “MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field”
  • must_have “Proficiency in Python and SQL with experience in production-quality code”
  • must_have “Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis”
  • nice_to_have “Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks”
pythonsqlstatistical methodsmachine learning
Role brief

Zoox is on an ambitious journey to develop a full-stack autonomous vehicle system for cities. We are seeking a Senior 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. This is a deeply technical and hands-on role where you will be expected to be a self-sufficient builder and coder, not just a manager of projects. 

In this role, you will:

  • 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.

  • Qualifications:

  • 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
  • Bonus Qualifications:

  • 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