Senior Data Scientist

Overview

Join Wayve as a Data Scientist working with AI Engineers to turn data into actionable direction for the Wayve AI Driver. You’ll design experiments, analyze real and simulated driving data, and help improve safety and performance at scale. Your work will influence product requirements, prioritization, and cross-functional strategy in a fast-moving, mission-driven environment. This role blends rigorous data science with practical, production-facing impact, within a hybrid London-based team.

Pay / Benefits

• market salary benchmarking
• equity/ownership
• relocation and visa sponsorship
• hybrid working with core hours
• learning and development budgets
• comprehensive benefits including health and wellbeing

Responsibilities

• Define and refine performance metrics to align engineering effort with commercial goals
• Design and run experiments and measurements to ensure product requirements meet safety and performance targets
• Investigate bottlenecks in model training and inference to enable improvements
• Test and validate hypotheses to drive Wayve AI Driver enhancements
• Summarise and visualise findings to support prioritisation and strategic decisions
• Collaborate with engineering and cross-functional partners across time zones to communicate insights

Key requirements

• 3+ years of experience in a Data Science role
• Fluent in querying and building large datasets; production-level SQL for data pipelines
• Experience designing real-world experiments (e.g., A/B tests) and evaluating test statistics
• Strong statistical foundations and understanding of frequentist statistics
• Proficiency in Python with data science packages (pandas, sklearn, statsmodels, scipy) or R with relevant packages
• Ability to summarise and communicate findings clearly and compellingly
• Track record of influencing team direction with actionable insights
• Comfort working asynchronously across time zones with cross-functional partners
• Curiosity about building something new and contributing to AV2.0

• strong communication
• influence and collaboration
• curiosity and bias toward actionable insight
• SQL
• Python (pandas, scikit-learn, statsmodels, scipy)
• R (dplyr, caret)

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