Data Scientist, Behavior Evaluation
TLDR
Shape highway-planner safety and efficiency by designing advanced experimental frameworks, modeling tail risks, and crafting KPI-driven validation from large-scale simulation and real-world data.
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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.
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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.
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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.
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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.
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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).
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Experience: 3–6+ years of professional experience as a Data Scientist or Quantitative Engineer, with a proven track record of landing data-driven impact.
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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.
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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.
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Robotics or Autonomy Background: Experience analyzing spatial-temporal data, sensor logs, or vehicle telemetry from robotics, autonomous vehicles, or aviation systems.
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Simulation-Based Testing: Familiarity with validating software systems using empty-world or simulation platforms at scale.
- Modern Data Stack: Experience with workflow orchestration tools (e.g., Airflow) and building advanced data visualization layers (e.g., Superset).
Benefits
Equity Compensation
Amazon RSUs
Health Insurance
Other Benefit
life insurance
Paid Time Off
paid time off (e.g. sick leave, vacation, bereavement)
Stock Options
Zoox Stock Appreciation Rights
Zoox is building a fully autonomous vehicle fleet from the ground up, coupled with the ecosystem necessary to launch this technology into urban environments. By integrating robotics, machine learning, and innovative design, Zoox is paving the way for a new era of mobility-as-a-service.
- Founded
- Founded 2014
- Employees
- 500+ employees
- Industry
- Automotive
- Total raised
- $990M raised