Zoox

Autonomy Software

DataScientist,BehaviorEvaluation

$176–240k Foster City, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“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.

Autonomy Software
Problems you'll solve

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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