Zoox
Autonomy Software
DataScientist,BehaviorEvaluation
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Scientist, Behavior Evaluation at Zoox. Skills: Statistical modeling, Experimental design, Data analysis, Behavior evaluation. Design advanced experimental frameworks. Formulate robust statistical models”
What You'll Achieve.
Rigorously validate highway planner behavior; Accurately model and predict edge cases; Proactively identify statistical anomalies; Isolate low-frequency, high-severity edge cases; Isolate systemic Autonomy engineering debt; Translate findings into actionable recommendations
Industry & Context.
Isolate edge cases; Identify anomalies
What They're Looking For.
Must Have
Bachelor's or Master's degree in quantitative field, 3–6+ years of professional experience, Deep understanding of hypothesis testing, Deep understanding of experimental design, Deep understanding of regression analysis, Deep understanding of non-parametric/resampling methods, Deep understanding of time-series analysis, High proficiency in Python, Ability to write complex SQL queries, Exceptional ability to articulate complex mathematical methodologies, Exceptional ability to articulate statistical results
Nice to Have
Robotics or Autonomy Background, Experience analyzing spatial-temporal data, Experience analyzing sensor logs, Experience analyzing vehicle telemetry, Familiarity with simulation-based testing, Experience with workflow orchestration tools, Experience building advanced data visualization layers
What You'll Do.
Design advanced experimental frameworks
Formulate robust statistical models
Formulate hypothesis testing frameworks
Formulate quasi-experimental designs
Architect scenario-based metrics
Own and mature behavioral KPIs
Analyze complex driving scenarios
Identify statistical anomalies
Surface statistical edge cases
Apply data mining techniques
Apply advanced statistical techniques
Isolate low-frequency
high-severity edge cases
Isolate systemic Autonomy engineering debt
Drive cross-functional alignment
Translate complex statistical findings
Translate multi-source evaluations
Provide actionable technical recommendations
Collaborate with Autonomy Software Engineers
Collaborate with Safety Systems
Collaborate with Product teams
How You'll Work.
Team & Collaboration
Cross-functional alignment; Autonomy Software Engineers; Safety Systems; Product teams
Communication Scope
Articulate complex methodologies; Articulate statistical results
Full Job Description
## In this role, you will 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. ## Qualifications 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 a
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