Data Scientist, Zoox

• Education: Bachelor’s or Master’s degree in a highly quantitative field (e.g., Statistics, Mathematics, Data Science, Operations Research, or a related field with a strong statistical focus)
• Experience: 3–6+ years of professional experience as a Data Scientist or Quantitative Engineer, with a proven track record of landing data-driven impact
• Strong Statistical Foundations: Deep understanding of hypothesis testing, experimental design, regression analysis, non-parametric/resampling methods (e.g., bootstrapping, permutation tests), and time-series analysis handling autocorrelated data
• Strong Programming: High proficiency in Python (Pandas, NumPy, SciPy, scikit-learn) and the ability to write highly complex, optimized SQL queries for massive distributed databases
• Communication: Exceptional ability to articulate complex mathematical methodologies and statistical results to cross-functional engineering partners
• You do not need to match every listed expectation to apply for this position

Desirable

• As a Data Scientist on the Behavior Evaluation team, you will be the statistical anchor ensuring our autonomous driving systems navigate highway environments with world-class safety, efficiency, and comfort
• Highway evaluation presents a unique industry challenge: verifying vehicle behavior at high velocities where the margin for error is razor-thin, and critical edge cases are buried in petabytes of data
• In this role, you will bridge advanced statistical methodology with scalable software engineering
• You will design the mathematical frameworks, statistical tests, and data-driven metrics that evaluate our planner's decisions
• Working directly with large-scale simulation and real-world fleet data, your insights will define our validation pipelines, identify behavioral regressions, and directly shape the software powering our next-generation autonomous fleet
• Design Advanced Experimental Frameworks: Formulate robust statistical models, hypothesis testing frameworks, and quasi-experimental designs (such as synthetic controls or matching) to rigorously validate highway planner behavior in simulation and shadow-mode deployments
• Model Tail Risks & Rare Events: Use Surrogate Safety Measures (e.g., TTC, PET) to accurately model and predict low-frequency, high-severity edge cases that traditional mean-based statistics miss
• Architect Scenario-Based Metrics: Own and mature critical behavioral KPIs, utilizing data stratification to analyze complex driving scenarios (e.g., high-speed merging, cut-ins) while proactively identifying statistical anomalies like Simpson’s Paradox
• Surface Statistical Edge Cases: Apply data mining and advanced statistical techniques to isolate low-frequency, high-severity edge cases and systemic Autonomy engineering debt
• Drive Cross-Functional Alignment: Translate complex statistical findings and multi-source evaluations into clear, actionable technical recommendations, collaborating closely with Autonomy Software Engineers, Safety Systems, and Product teams