Senior Data Scientist, Systems Performance
New
M
MotionalAutonomous vehicles
Remote U.S.Full-TimeSenior
Salary$149,000 — $198,500 USD
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Job Details
- Experience
- 5+ years of industry experience
- Required Skills
- AWSPythonSQLData AnalysisMachine Learning
Requirements
- Have 5+ years of industry experience solving complex problems with large datasets.
- Hold a bachelor’s or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field.
- Demonstrate strong Python and SQL skills and experience using data analysis libraries with large, complex datasets.
- Have experience applying advanced statistical and machine-learning methods to large datasets.
- Demonstrate experience with statistical analysis, hypothesis testing, causal analysis, and data analysis.
- Be able to work independently with minimal guidance and drive projects from problem definition to actionable results.
- Explain technical findings clearly to engineering partners and leadership.
- Preferred: master's or PhD.
- Bonus: experience with adversarial scenario generation and closed-loop simulation environments.
- Bonus: autonomous driving or robotics experience evaluating perception, prediction, or motion-planning subsystems.
- Bonus: familiarity with data pipelines and distributed compute such as AWS.
- Bonus: expertise in machine learning, deep learning, sequence modeling, or probabilistic ML and uncertainty quantification.
- Bonus: familiarity with ISO 26262 or ISO 21448 (SOTIF).
Responsibilities
- Lead development of evaluation frameworks and metrics to measure and validate autonomous-system performance.
- Collaborate with Functional Safety and Systems Engineering to map evaluation metrics to automotive safety standards and launch readiness decisions.
- Monitor evaluation metrics and performance data for drift, inconsistencies, and degradation.
- Apply statistical methods to simulation and on-road data and develop new methods for analyzing AV performance.
- Partner with triage operators and simulation engineers to convert disengagements and edge cases into scenarios for the simulation catalog.
- Use fleet and evaluation data to identify edge cases and coverage gaps and strengthen test coverage with engineering partners.
- Establish correlations between on-road and simulation data and develop metrics to investigate trends and anomalies.
- Communicate findings to technical leaders and stakeholders and establish a self-service model for developers.
- Introduce machine-learning methods for performance evaluation where they add rigor and scale.
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