{"id":105869,"url":"https://alion.io/job/zoox-senior-data-scientist-verification-validation","title":"Senior Data Scientist - Verification & Validation","company":{"id":3626,"name":"Zoox","domain":"zoox.com","url":"https://alion.io/company/zoox","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":72,"open_postings":45,"ghost_share":0,"stale_share":0.911,"repost_share":0,"time_to_fill_p50_days":83,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Foster City, United States","Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":209000,"max":285000,"currency":"USD","period":"year","gross":null,"usd_annual":285000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Verification & Validation","optional":false}],"status":"live","first_seen_at":"2026-03-27T03:12:46Z","employer_posted_date":"2026-03-27","last_verified_at":"2026-10-26T19:58:34Z","board_verified":true,"closed_at":null,"days_open":213,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":213},"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.\nYou 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.\nIn this role, you will:\nDesign Evaluation Frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.\n\nConduct Robust Analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.\n\nInform 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.\n\nDefine Metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.\n\nLead the Lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.\n\nScale Pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.\n\nQualifications:\nMS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field\nProficiency in Python and SQL with experience in production-quality code\nDemonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.\nExperience with large-scale data analysis and statistical modeling\nProficiency with Git, unit testing, and collaborative development practices\nBonus Qualifications:\nHands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines\n\nExperience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks\n\nExperience with designing metrics and delivering actionable insights that drive business 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