Senior Data Scientist
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will support AI engineers by analysing real and simulated driving data. You will develop performance metrics, design experiments and targeted measurements, and investigate model-training and inference bottlenecks. You will validate hypotheses and communicate actionable findings that improve functionality, safety, and performance.
Requirements
- 3+ years of experience in a data science role
- SQL and large-dataset querying experience
- Experience designing real-world experiments and evaluating test statistics
- Knowledge of statistical distributions and frequentist assumptions
- Proficiency with Python or R and data science or machine learning packages
- Data summarisation, visualisation, and communication skills
- Experience influencing team direction through findings
- Experience deriving actionable insights for prioritisation and strategy
- Experience working asynchronously across time zones with cross-functional partners
- Machine learning experience with PyTorch
- Experience applying research ideas to production
- Experience with statistical rigour and experimental best practices
- Experience with causal inference, econometrics, or Bayesian hypothesis testing
- Experience with distributed computing such as Spark or Hadoop
- Experience in a fast-moving technology company or startup
Responsibilities
- Formulate and refine performance metrics
- Design experiments and targeted off-road measurements
- Investigate training and inference bottlenecks
- Identify and validate hypotheses for model improvements
Benefits
- Competitive equity package
- Hybrid working arrangement
- Core working hours with flexibility to determine a schedule with the team
