Key Notes
Three-plus years managing at least five individual contributors and a strong applied data-science foundation, including causal inference, are essential. The manager leads a UK team turning real and simulated driving analysis into evaluation methods and engineering insight for Wayve’s Autonomy organisation. The London role is hybrid with two office days each week and includes roadmap ownership, technical review, hiring and team development.
What You'll Work On
- Set three, six and twelve-month roadmaps with Autonomy stakeholders, balancing customer demand, technical priorities and available team capacity.
- Review statistical proposals and evaluation designs with technical leads, maintaining standards for causal rigour and measurement fidelity.
- Monitor delivery cadence, surface schedule slippage and negotiate customer trade-offs across experimental and observational analysis work.
- Hire and onboard data scientists while investing in tooling and automation that scale the applied-methods function beyond headcount.
- Guide career development and build cross-functional relationships that keep the team aligned with current and emerging Autonomy needs.
Why This Role Matters
- Autonomy engineering teams use analysis of real and simulated driving to progress towards customer commitments; statistical rigour and evaluation fidelity shape the insights available to those teams.
- Customer commitments compete with finite data-science capacity; roadmap and delivery management determine which evaluation questions receive analysis within each planning horizon.
What They Are Looking For
- Experience: At least three years managing a team of five or more individual contributors is essential.
- Technical: A strong data-science foundation, including experimental or observational causal-inference methods, is essential.
- Experience: Setting technical direction and reviewing designs and proposals from senior engineers is essential.
- Stakeholder: Translating cross-functional stakeholder and customer needs into roadmaps, priorities and delivery expectations is essential.
- Preferred: Applied data-science scaling, simulation, ML evaluation, PyTorch, statistical practice, large datasets or distributed computing experience is desirable.