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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The Senior Data Scientist role sits on the Model Integration & Release team, which ships a new driving model baseline weekly and runs simulation and on-road testing to validate model candidates.
In this role, you will own the analytical layer that turns evaluation outputs into findings that improve both models and measurement methodology. You will lead analysis of model and autonomous-vehicle performance data, translating complex signals from simulations, offline evaluation, and on-road testing into clear recommendations for engineering teams. Working autonomously within a fast-moving weekly release cadence, you will investigate performance issues, apply statistical rigor to evaluation data, and identify gaps in metrics, test coverage, or scoring logic.
Key responsibilities include:
- Defining what deeper analysis means in practice and building tooling, workflows, and habits to support it
- Improving how driving performance is measured by identifying blind spots, inconsistencies, and gaps in simulation and offline metrics
- Investigating on-road release-test findings and determining whether offline tests should have caught them
- Performing experiment analysis and applying statistical methods to provide confident, data-driven promotion recommendations for new driving models
- Expanding performance measurement beyond intervention rates using behavioral and operational signals
- Partnering with Data Science and Validation teams on evaluation methods and ground truth definition
- Supporting partner-facing quality investigations by quantifying and tracking issues from OEM QA workflows
- Documenting findings and recommendations in a way the team can act on within the weekly release cadence
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company emphasizes real ownership and is still writing much of how it works—suited for people who want autonomy and impact over a fully-structured environment.
Requirements:
- Strong analytical and investigative skills; ability to move from vague observations to clear, evidence-backed conclusions
- Hands-on experience with ML evaluation data, driving-performance metrics, or complex test results at scale
- Experience applying statistical methods for A/B testing and experimentation (confidence intervals, distributions, sampling)
- Proficiency in Python or SQL with comfort querying and analyzing large datasets
- Ability to operate autonomously in a fast-moving environment, setting priorities and knowing when to solve directly versus escalate
- Clear written communication; ability to turn messy investigations into concise, actionable recommendations for technical and non-technical audiences
- Genuine curiosity about model behavior and measurement methodology
Beneficial experience: autonomous driving, robotics, safety-critical ML systems, simulation-based testing, on-road experiment analysis, OEM or partner validation workflows, or evaluation infrastructure and measurement methodology.
