Data & AnalyticsHybrid

Senior Data Scientist - Verification & Validation

Zoox · Hybrid

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Last seen by MeritLog September 12, 2026Source: LeverSource version: lever-postings-v1

MeritLog read this listing from Zoox's Lever job board and last checked it on September 12, 2026.

Source: the employer's Lever job board. Open the original listing for current details.

Job details

Work model: Hybrid
Salary: $209,000 - $285,000
Location: Foster City, CA; Boston, MA
Occupation: Data Scientists(O*NET 15-2051.00)

Hiring context

How this role compares at Zoox

Zoox has 245 live roles in MeritLog’s catalog across 9 job families, and 67 of them are in data & analytics. 239 of those listings publish a pay range, a disclosure rate of 98%.

This role's posted range of $209,000 - $285,000 sits above 69% of the 223 other Zoox roles quoted over the same currency and period.

Zoox concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What they're asking for

MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative fieldEducation
Proficiency in Python and SQL with experience in production-quality codeSkill
Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.Skill
Experience with large-scale data analysis and statistical modelingSkill
Proficiency with Git, unit testing, and collaborative development practicesSkill

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

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

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