Safety Statistician, III – Risk & Safety Analysis

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 statistics and decision support, not dashboarding, experimentation platforms, or generic ML product analytics.

Responsibilities

Employ statistically sound 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 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

Requirements

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; 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; 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

Nice to have

SQL; R; Experience applying Bayesian methods to estimate risk using disparate data sources (such as simulations and naturalistic driving); 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

Benefits

competitive compensation package including bonus component and stock options; 100% paid medical, dental, and vision premiums for full-time employees; 401K plan with a 6% employer match; flexibility in schedule and generous paid vacation; company-wide holiday office closures; AD+D and Life Insurance