Aurora is working to deliver the benefits of self-driving technology safely, quickly, and broadly. We are searching for a Senior Safety Data Scientist to support the development of quantitative safety arguments and tooling infrastructure. You will apply statistical analysis, machine learning, and probabilistic modeling to inform safety strategy and support external safety communications. You will collaborate with stakeholders across software, systems, hardware, data science, data infrastructure, operations, product, legal, and government relations to shape the future of autonomous vehicle safety. In This Role, You Will: Design and deploy safety related data pipelines, models, tooling, and infrastructure in partnership with technical and non-technical stakeholders. Execute in-depth safety analysis on operational and simulation data, distilling complex findings into immediate, actionable risk mitigation strategies. Implement safety-related initiatives in alignment with safety goals and best practices. Develop safety related processes and reporting for organizational, operational, and product data to ensure accuracy and consistency. Support the identification of systemic, latent, and emerging risks in fleet operations to advance root cause analyses and implement appropriate risk management measures. Define and evolve safety metrics by designing and validating novel measures using hypothesis testing and exploratory analysis. Audit and validate technical safety evidence and artifacts spanning the breadth of self-driving technology (hardware, software, and operations) to ensure alignment with quantitative safety arguments and regulatory standards through enterprise tool suites Conduct root cause analysis for safety issues and support the formulation of specific, data-backed corrective actions. Lead the quantitative development of internal and external reports (e.g., Voluntary Safety Self Assessment) and public statements, ensuring safety arguments are statistically robust. Present and expertly communicate the output of technical analysis to both technical and non-technical audiences. Development of new processes and procedures related to safety metrics, data integrity, and issue handling.