Bayesian Statistician – Risk & Safety Analysis

About the Company     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.    Meet the Team   As a Safety Statistician – Risk & Safety Analysis, you will play a critical role in how Torc evaluates, communicates, and makes decisions about the safety of its autonomous driving systems. You will influence the design and execution of statistically rigorous analyses that inform safety assurance strategies, engineering priorities, and risk-based decision making. Your work will directly influence how safety performance and risk are measured, understood, and acted upon across the organization. This is a technical role focused on applied Bayesian and frequentist statistics and decision support, not dashboarding, experimentation platforms, or generic ML product analytics.   What You’ll Do   Employ statistically sound Bayesian and frequentist analyses to answer high-impact safety and regulatory questions, including how system performance translates to risk Apply statistical methods to quantify and assess risk using a variety of data sources including large-scale time-series data (e.g., vehicle and sensor data) and structured safety datasets Bridge safety and engineering teams by translating complex Bayesian and frequentist analyses into information engineers can act on Develop automated, production-ready analysis workflows that support continuous safety monitoring Select and defend appropriate statistical approaches for sparse, noisy, or rare-event data, applying Bayesian and frequentist methods and leveraging machine learning techniques where appropriate Communicate statistically defensible findings to technical leaders, safety stakeholders, and executives   What You’ll Need to Succeed   Bachelor’s Degree in Statistics, Computer Science, Robotics, Engineering, or related technical field plus competences typically acquired through 6+ years of experience; OR Master’s Degree in a related technical field plus competences typically acquired through 3+ years of experience. Strong background in applied statistics, safety analysis, and risk estimation Demonstrated experience applying Bayesian analyses A solid understanding of both Bayesian and frequentist statistical frameworks with the ability to select the right approach for each problem Demonstrated ability to assess whether Bayesian credible intervals have adequate frequentist coverage properties for decision-making, and adjust priors or model structure when they dont Experience in autonomous vehicles, adjacent safety-critical domains (automotive, aerospace, defense, robotics, rail, etc.), or comparable actuarial experience Experience working with complex, real-world datasets rather than clean or purely academic data Hands-on experience using Python for analysis (SQL and/or R a plus, but not required) Ability to communicate statistical concepts clearly to non-statistical audiences Comfort operating independently as a technical leader in a cross-functional, distributed environment Domain knowledge in Bayesian methods   Bonus Points   Experience applying Bayesian methods to estimate risk using disparate data sources (such as simulations and naturalistic driving) Knowledge of conservative Bayesian inference principles and their application to safety-critical decision-making Experience with causal inference methods or Bayesian networks for understanding system dependencies  Advanced knowledge of MCMC methods (Hamiltonian Monte Carlo, adaptive sampling, convergence diagnostics), variational inference, and other Bayesian computational techniques to incorporate uncertainty from multiple data sources when the posterior distribution of the safety estimate does not have a closed-form solution Background applying statistics to engineering or physics-based systems Familiarity with time-series analysis, uncertainty quantification, or rare-event modeling Experience supporting executive or external stakeholder decision-making requiring quick turnarounds, balancing analytical rigor with timeliness   Perks of Being a Full-time Torc’r   Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:     A competitive compensation package that includes a bonus component and stock options 100% paid medical,