Director of Safety Data Analysis

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

At Torc Robotics, our Safety Data Analysis team sits at the core of how we measure, understand, and continuously assess the safety performance of Torc Drive. This team blends engineering, statistics, and large-scale data analysis to transform complex, multi-modal datasets into actionable safety insights.

• →Team Leadership, Development, and Execution
  • →Leads, mentors, and develops a team of safety analysts and engineers, establishing clear roles, responsibilities, and performance expectations across on‑road and simulation‑based safety performance monitoring.
  • →Provides regular technical review, coaching, and feedback to ensure analyses are statistically sound, methodologically defensible, reproducible, and supported by clear documentation.
  • →Sets expectations for rigorous problem formulation, appropriate method selection, and defensible interpretation of results, particularly for sparse, noisy, or rare‑event safety data.
  • →Ensures continuity of knowledge through documentation, peer review, cross‑training, and shared analytical patterns to reduce single‑point dependencies.
• →Safety Performance Governance and Decision Support
  • →Ensures team outputs are aligned with internal safety policies, safety case strategy, and regulatory expectations, including appropriate use of statistical and risk‑based evidence.
  • →Applies engineering judgment, informed by an understanding of safety metrics, deployment context, and probabilistic risk estimation, to evaluate the sufficiency of safety performance evidence and residual risk.
  • →Guides leaders in understanding the strengths, limitations, uncertainty, and assumptions underlying safety performance results.
  • →Balances analytical rigor with timeliness to support high‑consequence, time‑sensitive decisions, without overstating confidence or masking uncertainty.
  • →Drive data visualization and reporting strategies that enable both technical teams and executives to quickly understand insights and tradeoffs
  • →Ensure transparency and traceability through strong documentation, reproducible workflows, and automated reporting (including regulatory artifacts)
• →Performance Monitoring and Investigation Leadership
  • →Establishes and oversees requirements and safety metrics including thresholds and targets for safety performance monitoring across on‑road and simulation‑based data sources.
  • →Ensures performance monitoring approaches appropriately reflect real‑world deployment exposure and evolving operational contexts.
  • →Governance of continuous analysis workflows that support operational monitoring, milestone gating, and incident response.
  • →Ensures investigations are unbiased, statistically appropriate, and methodologically sound, including correct treatment of rare events, sparse data, and uncertainty.
  • →Reviews and challenges analytical assumptions, metric definitions, and risk interpretations to prevent misplaced confidence.
  • →Guides the team in synthesizing findings across data domains into coherent, defensible safety narratives that connect performance results to risk.
• →Communication, Influence, and Stakeholder Alignment
  • →Communicates complex safety performance results, statistical reasoning, and risk implications clearly and confidently to engineers, executives, and safety governance bodies.
  • →Partner cross-functionally with engineering, product, verification & validation, simulation, metrics implementation, and safety teams to integrate workflows and align on data-driven decisions
  • →Represents the team in cross‑functional forums, advocating for analytical rigor, disciplined interpretation of results, and realistic expectations of what performance data can and cannot support.
  • →Translates uncertainty, assumptions, and limitations transparently, enabling informed decision‑making rather than false confidence.
  • →Builds trust through consistency, technical credibility, and demonstrated accountability for the quality and integrity of team outputs.

• Degree in Engineering, Computer Science, Statistics, or a related field
• PhD + 3+ years, MS + 7+ years, or BS + 10+ years of relevant experience
• Experience with Federal and State crash and safety databases
• Experience in developing safety metrics including thresholds and targets
• Proven leadership experience building and scaling data science or analytics teams in complex, technical environments.
• 5 years of leadership experience
• Strong background across data science, statistical analysis, and engineering principles, ideally applied to physical systems or transportation
• Experience with Python (data science, engineering, visualization libraries) and SQL
• Deep understanding of statistical methods, experimental design, and performance evaluation
• Experience working across multiple data domains (sensor data, video, physics-based data, etc.)
• Familiarity with modern development practices (CI/CD, code reviews, shared repositories)
• Strong communication skills with the ability to translate complex analyses into clear, actionable insights
• Highly analytical and naturally curious, with the ability to break down ambiguous problems and drive structured solutions

• Background in systems safety, safety engineering, or transportation safety analysis
• Experience in autonomous vehicles, robotics, or automotive systems
• Exposure to large-scale simulation, perception, or data-centric AI workflows