Director of Data Science

Job description

Job responsibilities

• Rapid Operational Analytics: Execute fast-turnaround investigations on emergent fleet issues, converting real-time operational data into high-impact insights for launch decisions.
• Advanced Exposure Modeling: Build complex statistical models to evaluate vehicle safety and performance across diverse environmental variables and Operational Design Domains.
• Cross-Functional Partnership: Serve as the primary analytical anchor for Systems and Autonomy teams (Perception, Prediction, Planning).
• Executive Artifacts: Work with other systems teams to distill raw telemetry and rapid-response findings into high-level strategy documents, visualizations, and dashboards.
• Team Leadership: Mentor and scale a dedicated four-person team, instilling a culture that pairs rapid analytical agility with absolute scientific rigor.

Job requirements

• Industry Experience: 10+ years of professional experience leading data science or applied AI/ML initiatives, with at least 5 years explicitly scaling data-driven programs for autonomous driving, robotics, or complex…
• Educational Background: Master’s degree or PhD in Data Science, Computer Science, Robotics, Mathematics, Physics, or a highly quantitative field.
• Expertise in Advanced Analytics: Deep foundational knowledge in statistical analysis, density estimation, hypothesis testing, uncertainty quantification, and causal reasoning.
• Technical Proficiency: Mastery of Python, SQL, and data science infrastructure libraries, alongside a strong understanding of modern sequence modeling (Transformers) and distributed compute scaling frameworks (e.g.,…
• System Deconstruction: Demonstrated track record of translating ill-defined, ambiguous real-world performance questions into structured metric designs and concrete solution architectures.
• Exceptional Communication: Proven capacity to articulate highly technical data patterns and trade-offs into compelling strategies for cross-functional engineering peers and C-suite executives.
• Collaborative Leadership: Strong interpersonal ability to wield cross-functional technical influence, resolve technical disagreements across teams, and establish organizational best practices around data integrity.