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Salary: USD 126,100 - 151,300 / annual
Torc Robotics, now part of the Daimler family, is a leader in autonomous driving software for commercial trucks. The Safety Data Analysis team sits at the core of measuring and continuously assessing the safety performance of Torc Drive, blending engineering, statistics, and large-scale data analysis to transform complex, multi-modal datasets into actionable safety insights.
In this role, you will develop, implement, and refine safety performance indicators, thresholds, and targets to proactively identify, assess, and manage safety risk across on-road and simulation-based data sources. You'll apply statistically sound methods to evaluate safety performance, quantify uncertainty, and support risk-based decision-making in complex, real-world operational contexts. A key responsibility is developing automated, production-ready analysis workflows that support continuous safety monitoring, milestone gating, incident response, and safety performance evaluation.
You will ensure performance monitoring approaches appropriately reflect real-world deployment exposure, evolving operational contexts, and relevant safety requirements. This requires balancing analytical rigor with timeliness to support high-consequence, time-sensitive decisions. You'll evaluate data quality, uncertainty, bias, and limitations to ensure findings are statistically defensible and useful for decision-making.
Cross-functional collaboration is essential: you'll partner with engineering, product, verification and validation, simulation, metrics implementation, and safety teams to integrate workflows and align on data-driven decisions. You'll support data visualization, reporting, and communication strategies that help technical teams, safety stakeholders, and executives understand insights, tradeoffs, and limitations. Strong documentation of analytical methods, assumptions, workflows, requirements, safety metrics, and outputs is critical for traceability, collaboration, and regulatory readiness.
Required qualifications include an advanced degree (B.S. with 5+ years, M.S. with 3+ years, or PhD with 1+ year) in Data Science, Computer Science, Math, or related field. You need proven experience in mechanical engineering, vehicle engineering, autonomous systems, robotics, or physics-based applications. Strong proficiency in Python and SQL, experience with large-scale time-series data, vehicle/sensor data, and cloud-based data technology is essential. Background in applied statistics, safety analysis, risk estimation, or decision support is required. Bonus qualifications include time-series analysis, uncertainty quantification, rare-event modeling, reliability analysis, and experience supporting regulatory or executive decision-making.
