Director, Data Science

About the role

You will own the end-to-end data science lifecycle for May Mobility's autonomy stack, translating fleet behavior and simulation outcomes into actionable engineering decisions. You will define and drive a data strategy that spans synthetic data generation, perception and planning model evaluation, and fleet operations analytics. You will act as the primary interface between the data science organization and cross-functional leaders such as Engineering, Product, Safety, and Operations. In this role, you will set measurement standards that directly influence what capabilities are released to the public roads. You will be responsible for building a high-performing data science team capable of operating at the pace and rigor required for production autonomous vehicles. You will champion the translation of research-grade machine learning into transit-grade systems that are safe, reliable, and scalable.

Key facts

What you'll do

Define and own the data science roadmap for simulation, synthetic data, perception, prediction, planning, and fleet analytics, with clear 12-24 month milestones and measurable outcomes.

Recruit, lead, and develop a team of senior data scientists, ML engineers, and managers, establishing a high bar for talent and building a compelling brand against AV and AI competitors.

Partner with Engineering, Product, Safety, and Operations to define release criteria, performance metrics, and operational design domain expansion decisions grounded in data.

Drive machine learning and analytics applications from dataset curation and scenario definition through modeling, offboard evaluation, productionization, and continuous fleet monitoring.

Establish company-wide measurement and experimentation standards, including before/after analyses, simulation-based A/B comparisons, and statistically credible reporting on real-world incidents.

Lead rigorous code and design reviews to ensure engineering quality and reproducibility across data science systems and workflows.

Track and analyze technical performance of the autonomy stack in deployed operations, surface root causes, and prioritize fixes in collaboration with engineering teams.

Provide technical guidance to Engineering and Operations leaders on diagnosing issues and selecting ML changes that most effectively improve safety and service metrics.

Represent May Mobility's data science impact externally through publications, conference talks, partner engagements, and high-stakes recruiting conversations.

Own the alignment between simulation test coverage and real-world operational requirements, ensuring scenarios reflect evolving city dynamics and edge cases.

Champion data infrastructure investments that enable scalable dataset management, rapid experimentation, and reliable decision-making across the organization.

Translate complex analytical findings into clear narratives and decisions for executives, regulators, city partners, and internal stakeholders.

Ensure that data practices support compliance, auditability, and safety case development without compromising speed of innovation.

Foster a culture of curiosity, ownership, and evidence-based decision-making within the data science and broader autonomy teams.

Requirements

Demonstrated experience scaling a data science function within a hard-tech or robotics environment, with a track record of delivering production ML systems.

Strong background in machine learning, statistical modeling, and data infrastructure, with hands-on experience in at least one modern data stack.

Expertise in simulation-based evaluation, scenario generation, and performance metrics relevant to autonomous vehicle systems.

Proven ability to lead cross-functional initiatives and influence engineering and product decisions through data and analysis.

Exceptional written and verbal communication skills, with experience presenting complex methods and results to both technical and non-technical audiences.

Experience recruiting, mentoring, and retaining technical talent in competitive technical domains such as robotics, AI, or autonomous vehicles.

A demonstrated commitment to safety-critical systems, including familiarity with functional safety concepts, monitoring, and incident analysis in deployed fleets.

Bachelor's or advanced degree in Computer Science, Electrical Engineering, Robotics, Statistics, or a closely related quantitative field.

Nice to have

Experience in the autonomous vehicle, robotics, or transportation technology sectors, with direct exposure to fleet operations and safety-critical ML.

Background in public transit or shared mobility domains, including familiarity with transit operations, scheduling, and accessibility considerations.

Practical notes

This is a full-time role based in the United States with remote flexibility as defined by company policy. Travel may be required for team meetings, industry conferences, or operational reviews as necessary. Employment eligibility to work in the United States is required, and sponsorship is not indicated at this time.