Lead Analyst

About the Role

We are building out an analytics function and are looking for a Lead Analyst with a strong leadership mindset and deep analytical expertise. You will own a major evaluation and experimentation focus area, help shape and grow the team, and work closely with cross-functional partners to influence technical and safety decisions.

While this role begins as an individual contributor, it is designed to quickly grow into a people-lead position. You will remain hands-on with analysis while guiding scope, methods, and technical rigor as the team scales.

What You’ll Do

• Develop deep system understanding: Analyze autonomous driving behavior and platform health across both simulation and real-world data. Define metrics and metric hierarchies that accurately capture system quality and readiness.
• Lead and grow the team: Help hire, mentor, and level up analysts. Set direction for an evaluation domain while partnering closely with technical leaders and teams building data infrastructure and analytics tooling.
• Design and run experiments: Lead experimentation efforts using rigorous statistical methods. Personally execute analyses, interpret results, and translate findings into clear, defensible decisions.
• Build scalable analytics: Work with data engineers and backend developers to assemble datasets and product ionize analysis pipelines, with attention to performance, reliability, and repeatability.

What You’ll Need

• Experience: 4+ years working in analytics for a large-scale or technically complex product or system
• Statistics: Strong foundation in statistical reasoning, experimental design, and methods for evaluating change and uncertainty
• Technical skills: Advanced Python and SQL; ability to independently design experiments and ship analyses and pipelines end to end
• Leadership: Demonstrated experience mentoring analysts, providing technical direction, and contributing to hiring and team growth
• Problem-solving: Comfort working on ambiguous, mathematically grounded problems at scale

Nice to Have

• Formal training in statistics or a related quantitative field (e.g., MS or PhD in Statistics, Applied Statistics, Biostatistics, Econometrics)
• Familiarity with modern data stacks and data engineering concepts
• Experience in autonomous systems or other highly technical, safety-critical domains