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Wayve is building the leading AI platform for autonomous driving using end-to-end AI that enables vehicles to learn directly 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 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 findings in accessible ways that support prioritization and strategy.
You will work asynchronously across time zones, communicate findings clearly and compellingly, and influence team direction through evidence-based insights. Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg, combining in-person collaboration with focused remote work.
The company is pioneering a mapless, hardware-agnostic AI platform that integrates with global OEM partners. Their approach focuses on building generalizable driving intelligence that can power any vehicle, anywhere, accelerating the transition to safer, more intelligent mobility.
**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 statistical scripting languages and data science/ML packages (Python with pandas, sklearn, statsmodels, scipy; or R with dplyr, caret, stats)
- Skilled at summarizing, visualizing, and communicating findings accessibly and compellingly
- Demonstrated ability to influence team direction through findings with 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
- Experience with large datasets and distributed computing (Spark, Hadoop, map-reduce tools)
- Background in fast-moving technology companies or startups
