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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. As a Senior Data Scientist, you will partner with AI engineering teams to develop actionable insights that guide improvements to the Wayve AI Driver through experimental and observational analyses of real and simulated driving.
Your day-to-day work involves collaborating closely with engineering and cross-functional partners to turn data into clear, practical direction. You will formulate and iterate on performance metrics that organize engineering efforts and guide progress toward commercial success. You'll design experiments and targeted off-road measurements to ensure delivery of customer product requirements while maintaining safety and performance. Your responsibilities include investigating factors in model training and inference that create bottlenecks in functionality and performance, identifying and validating hypotheses that unlock improvements to the AI Driver, and summarizing, visualizing and communicating findings in accessible ways that support prioritization and strategy.
You will work asynchronously across time zones, influence team direction through evidence-based findings, and bring a bias toward actionable insight that drives prioritization and strategy. This is a full-time hybrid role based in the London office with two days per week in-office attendance.
Wayve operates main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. The company is pioneering a mapless, hardware-agnostic AI platform that integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2+ through L4.
REQUIREMENTS:
- 3+ years of experience in a Data Science role
- Fluency in querying and building large datasets, including production-level SQL for data-transformation pipelines
- Experience designing robust real-world experiments (e.g., A/B tests) and critically evaluating test statistics
- Strong statistical foundations, including selecting appropriate distributions and testing assumptions behind frequentist statistics
- Proficiency in a statistical scripting language and data science/ML packages (e.g., Python with pandas, sklearn, statsmodels, scipy; or R with dplyr, caret, stats)
- Skilled at summarizing, visualizing and communicating findings in accessible and compelling ways
- Demonstrated ability to influence team direction through findings with a bias toward actionable insight
- Comfortable working asynchronously across time zones with cross-functional partners
- Deep curiosity about building something new and excitement about autonomous vehicle development
NICE-TO-HAVE:
- Practical machine-learning experience (e.g., PyTorch) and passion for taking research to production
- Track record of promoting statistical rigor and experimental best practices
- Experience with causal inference, econometric techniques, or Bayesian methodologies for hypothesis testing
- Experience with large datasets and distributed computing (Spark, Hadoop, map-reduce tools)
- Background in fast-moving technology companies or startups
