Analytics Engineer, AV Safety Engineering

General Motors is a leader in the automotive industry with a vision of zero crashes, zero emissions, and zero congestion. They are seeking an Analytics Engineer for their AV Safety Engineering team to develop analytics and metrics that support safety-related decision-making for automated driving systems. This role involves transforming complex data into actionable insights and working with cross-functional teams to enhance safety assurance.

Responsibilities:

• Define, prototype, and productionize safety and performance metrics for automated driving systems
• Establish analytic approaches and sufficiency criteria that support safety assessment, development decisions, and launch readiness
• Support proactive safety monitoring and targeted investigations tied to specific system-performance or safety questions
• Support systems, safety, testing, and verification stakeholders by comparing real-world and simulation-based results, identifying gaps, and helping improve the representativeness of evaluation methods
• Apply engineering and physics-based methods to process raw signals and derive meaningful representations of vehicle motion, driving context, and system behavior
• Distinguish sensor or pipeline errors from meaningful real-world outliers using engineering judgment and data validation methods
• Create interactive visualizations and reporting artifacts that communicate safety insights clearly, enhance transparency, and reduce barriers to interrogating source data in support of technical decision-making
• Build and maintain analytics infrastructure that supports safety assurance across development, validation, and deployment
• Develop reliable pipelines that ingest, transform, analyze, and publish data from vehicle systems, internal databases, simulation outputs, and external sources
• Optimize analytics code and workflows for scalable, automated cloud execution

Requirements:

• Bachelor's degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field, or equivalent practical experience
• 5+ years of experience analyzing large-scale driving, vehicle, robotics, or similar engineering data
• 5+ years of experience in ADAS, autonomous vehicles, robotics, or a related technical domain
• Experience with statistics relevant to large-scale engineering data analysis, including sampling, bias management, and experimental design
• Experience transforming noisy time-series or sensor data into analysis-ready features or metrics
• Strong problem-solving skills and a proactive, learning-oriented mindset
• Strong communication and collaboration skills, with the ability to work effectively across technical teams
• Strong programming skills in Python and SQL
• Experience building and operating cloud-based analytics or data-processing workflows at scale
• Experience in some combination of the following is expected: Programming & Frameworks: Python, SQL; Cloud & Big Data: cloud-based large-scale processing including notifications, queuing, serverless functions, event-driven processing, infrastructure as code, containerization, process monitoring, process optimization, identity and access management, and service-to-service access; Statistics: descriptive statistics, managing bias in large data mining activities, experimental design, and sampling strategies; DevOps / Infrastructure as Code: CI/CD, versioning, Docker, Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform; Data Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy
• Experience analyzing large-scale vehicle motion, driving context, automated-driving performance, or simulation data
• Experience with driver behavior modeling, human performance benchmarking, causal inference, or counterfactual modeling techniques
• Experience with systems engineering, verification and validation, simulation-based evaluation, scenario analysis, or work that bridges simulation and on-road safety assessment
• Experience building stakeholder-facing dashboards or interactive analytics products
• Experience with cloud or distributed data platforms, or with DevOps, CI/CD, containerization, or infrastructure-as-code workflows
• Publications, conference participation, or other demonstrated engagement in vehicle-safety, safety-analytics, or related technical work