May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. Job Summary May Mobility is entering an exciting phase of growth as we expand our autonomous transit and mobility services across the country. Founded in 2017 by a team of experienced roboticists, perception, behavior, AI, and software engineers, we operate driverless transit shuttles in real communities — not as a research demonstration, but as a daily-service product that people rely on to get to work, school, and home. The Director, Performance Analytics will lead Performance Analytics, the team that owns May Mobility's core performance KPIs end to end. You will own the company's core KPIs — safety, comfort, intervention-level, system health/drop, and related performance metrics — making sure there is one accurate, reproducible, trusted number for each; own experimentation and control, deep decomposition of any KPI on request, and V&V analytics; and apply machine learning selectively where it improves analysis quality. You will partner directly with Engineering, Product, Operations, and Safety leadership to set measurement standards, define release criteria, and translate frontline operating data into the next generation of our autonomy stack. Data Engineering owns the pipeline and tooling that feeds your team; you are their primary internal customer and set their requirements. This is a leadership role for someone who has scaled a performance analytics function inside a tech product/organization, who is comfortable making engineering and product tradeoffs alongside their team, and who sees the gap between research-grade ML and production transit-grade ML as the most interesting problem in the industry today. Essential Responsibilities • Set and own the Performance Analytics strategy across simulation and synthetic data to translate that strategy into a 12–24 month roadmap with measurable milestones. • Lead, grow, and develop a team of senior performance analysts, analysts with applied ML skills, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors. • Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when. • Drive deep decomposition of any KPI by geography, time, ODD element, or vehicle trend and anomaly detection • Improves analysis quality, and continuously monitors fleet performance. • Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents; determine what data is valid to analyze and make sure conclusions hold up before they reach leadership. • Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for analytics, reporting, and V&V systems. • Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners in language a non-technical stakeholder can act on. • Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the changes most likely to move our key safety and service metrics. • Represent May Mobility's performance analytics work externally where appropriate — through publications, conference talks, partner reviews, and recruiting. Skills and Abilities Success in this role typically requires the following competencies: • Autonomy Data Expertise. Can reason fluently about the data produced by a modern AV stack — sensor logs, perception outputs, planning traces, simulator results, and operational telemetry — and can identify which signals matter for which decisions. • Hands-On Technical Depth. Has personally shipped production analytics or applied-ML systems (e.g., trend/anomaly detection) within the last 3–5 years and is credible in code review and design review with senior engineers and scientists. • Cross-Functional Translator. Can explain a complex analytical or statistical finding to engineering, product, and executive audiences; and can extract a clear analytical brief from a vague business or safety question. • Data-Driven Decision Making. Uses fleet, simulation, and operational data to change roadmap decisions; comfortable defending a position with data and equally comfortable being wrong in front of the team when new data arrives. • Stakeholder Alignment. Builds durable working relationships with engineering, product, safety, and operations leaders, and with Data Engineering as the internal customer relationship that sets pipeline requirements without owning the pipeline; can broker disagreement between technical functions without escalation becoming the norm. • Talent Magnet and Coach. Has personally hired and developed senior analysts and front-line managers; calibrates the bar, gives direct feedback, and grows people into bigger jobs. • Prioritization Rigor. Comfortable killing work that doesn't earn its place on the roadmap; protects the team from low-leverage requests - including general-purpose data science support for other functions such as autonomy or release, which stays with those teams — while staying responsive to legitimate cross-functional needs. Qualifications and Experience Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience: • 8+ years of industry experience in performance/analytics, applied statistics, or data science, with at least 4 years managing senior individual contributors and front-line managers. • Direct experience leading performance analytics, experimentation, or V&V analytics work in at least one of the following domains: autonomous vehicles or ADAS, robotics, large-scale computer vision systems, safety/performance metrics, or statistical experimentation. • Demonstrated track record leading a team of 10 or more through a major delivery — for example, a production launch, a major model rollout, a regulatory milestone, or a significant ODD or product expansion. • Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Physics, Robotics, or a related quantitative field, or equivalent practical experience. • Strong programming skills in Python and SQL; working familiarity with statistical analysis and experimentation tooling, and enough ML fluency (e.g., trend/anomaly detection) to apply it selectively within analytics workflows. • Experience setting measurement and experimentation standards inside an engineering or product organization, with credible examples of metrics or evaluation frameworks the team adopted and kept using. • Experience operating in cross-functional partnership with engineering, product, safety, and operatio…