Director, Data Science

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, Data Science will lead the team responsible for turning the data generated by our fleet, simulation environment, and ML systems into the insights, evaluations, and decisions that make our autonomous service safer, more efficient, and ready to scale into new cities. You will own data science across simulation and synthetic data, perception and planning ML evaluation, fleet operations analytics, and the data infrastructure that supports them. 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.

This is a leadership role for someone who has scaled a data science function inside a hard-tech environment, 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.

• →Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.
• →Lead, grow, and develop a team of senior data scientists, ML engineers, 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 ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.
• →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.
• →Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science 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.
• →Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.
• →Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.

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 ML or analytics systems 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 ML 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; can broker disagreement between technical functions without escalation becoming the norm.
• Talent Magnet and Coach. Has personally hired and developed senior data scientists 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 while staying responsive to legitimate cross-functional needs.

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

• 8+ years of industry experience in data science, machine learning, or applied research, with at least 4 years managing senior individual contributors and front-line managers.
• Direct experience leading data science or ML work in at least one of the following domains: autonomous vehicles or ADAS, robotics, large-scale computer vision systems, simulation and synthetic data, reinforcement learning, or large-scale ML platforms.
• 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; working familiarity with the production ML stack used in modern AV/robotics environments (e.g., PyTorch or TensorFlow, distributed training, dataset and feature pipelines, experiment tracking).
• 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 operations leaders — comfortable both defending technical positions and adjusting them in light of business or safety constraints.

• Standard office working conditions which include but are not limited to prolonged sitting, standing and computer use
• Travel Required? Moderate: 11%–25% (site visits to deployment cities, partner meetings, recruiting events, and engineering offsites).

• Master's or PhD in Computer Science, Robotics, Statistics, EE, Mathematics, or a related quantitative field.
• Prior experience at an autonomous vehicle, robotics, or hard-tech company that has deployed products to real customers (not only research demos).
• Experience with simulation, synthetic data generation, sim-to-real transfer, or scenario-based evaluation for AV or robotics.
• Familiarity with safety-case construction, ODD definition, or regulator engagement for autonomous systems.
• Publications or conference contributions in top-tier ML, CV, or robotics venues (e.g., NeurIPS, ICML, CVPR, ICRA, RSS).
• Experience with C/C++ systems and/or GPU programming sufficient to engage credibly with onboard ML and infrastructure teams.
• Demonstrated ability to mentor and grow junior managers and senior individual contributors into bigger roles.