Data Scientist, Behavior Evaluation
Data Scientist on Behavior Evaluation team designing statistical frameworks and metrics to validate autonomous vehicle planner behavior on highways using simulation and fleet data. Requires 3-6+ years experience, strong Python/SQL skills, and expertise in experimental design and hypothesis testing.
Responsibilities
- Design advanced experimental frameworks including statistical models, hypothesis testing, and quasi-experimental designs (synthetic controls, matching) to validate highway planner behavior in simulation and shadow-mode deployments
- Model tail risks and rare events using Surrogate Safety Measures (TTC, PET) to predict low-frequency, high-severity edge cases
- Architect scenario-based metrics and own behavioral KPIs, utilizing data stratification to analyze complex driving scenarios while identifying statistical anomalies
- Surface statistical edge cases through data mining and advanced statistical techniques to isolate low-frequency, high-severity events and systemic engineering debt
- Drive cross-functional alignment by translating complex statistical findings into actionable technical recommendations for Autonomy Software Engineers, Safety Systems, and Product teams
Requirements
- Bachelor’s or Master’s degree in Statistics, Mathematics, Data Science, Operations Research, or related quantitative field with strong statistical focus
- 3–6+ years of professional experience as a Data Scientist or Quantitative Engineer
- Deep understanding of hypothesis testing, experimental design, regression analysis, non-parametric/resampling methods (bootstrapping, permutation tests), and time-series analysis for autocorrelated data
- High proficiency in Python (Pandas, NumPy, SciPy, scikit-learn) and ability to write complex, optimized SQL queries for massive distributed databases
- Exceptional ability to articulate complex mathematical methodologies and statistical results to cross-functional engineering partners
Nice-to-Haves
- Experience analyzing spatial-temporal data, sensor logs, or vehicle telemetry from robotics, autonomous vehicles, or aviation systems
- Familiarity with validating software systems using simulation platforms at scale
- Experience with workflow orchestration tools (Airflow) and data visualization layers (Superset)
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