Data Scientist

Austin, TX

Fully On-site

Starting at $150k Base Salary

About the Company

This team is advancing the future of autonomous driving by developing the metrics, evaluation frameworks, and analytical insights that ensure the safety, performance, and readiness of self-driving systems. Partnering closely with engineering and autonomy teams, they analyze both real-world and simulated driving data to enable data-driven decisions that accelerate commercialization.

About the Role

We’re seeking a Data Scientist to design and implement quantitative metrics that assess human behavior, safety compliance, and passenger comfort. You’ll work with large, complex datasets, apply statistical and experimental methods, and collaborate cross-functionally to ensure autonomous systems meet the highest standards of safety and reliability. This is a high-impact opportunity to shape how autonomy is measured, validated, and brought to market.

Responsibilities

• Analyze real-world and simulated autonomous vehicle data to evaluate system behavior and performance
• Develop and refine quantitative metrics (rule-based and ML-driven) to assess safety, driving quality, and passenger comfort
• Apply statistical inference and hypothesis testing to validate metric sensitivity, robustness, and reliability
• Partner with autonomy software, simulation, and safety teams to prioritize improvements using data-driven insights
• Build scalable data pipelines and tooling to automate metric computation, monitoring, and reporting
• Review driving logs and participate in test rides to ground analysis in real-world behavior and outcomes

Qualifications

• 3+ years of experience in data science, applied statistics, or metrics / evaluation development
• Proficiency in Python and SQL for analysis and data manipulation
• Strong background in statistical methods (e.g., regression, confidence intervals, A/B testing, hypothesis testing)
• Experience designing or evaluating metrics for complex or safety-critical systems
• Ability to communicate technical findings to cross-functional engineering and product stakeholders

Nice-to-Have's:

• Experience with machine learning (model evaluation, feature engineering, or offline experimentation)
• Familiarity with big-data tools (Spark, Hadoop, MapReduce)
• Knowledge of traffic regulations or automotive safety standards

*Must have existing authorization to work in the U.S.*