Data Scientist, Wayve

• 3+ years of experience in a Data Science role, with a focus on operations research, process automation and optimisation, or similar fields
• Proficient in querying and building large datasets, writing production-level SQL for data transformation pipelines
• Experience designing and evaluating real-world experiments (e.g., A/B testing) to optimize operations and performance
• Solid understanding of statistical principles, including hypothesis testing, distributions, and assumptions behind statistical methods
• Proficient in using a statistical scripting language (e.g., Python, R) and relevant packages (e.g., pandas, sklearn, statsmodels)
• Strong ability to summarize, visualize, and communicate data insights in a clear and compelling manner
• Proven track record of driving operational improvements and influencing team strategies with data-driven findings
• A focus on actionable insights that can directly inform fleet operations prioritization and optimization strategies
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply

Desirable

• As a Data Scientist in the Fleet Systems and Insights team, you will play a critical role in optimising fleet operations through data-driven insights and operational research. You’ll help identify high-impact opportunities and guide strategic decision-making, driving improvements across the on-road testing lifecycle
• Rather than focusing solely on black-box models, this role emphasizes using operational research techniques, experimental methods, and causal inference to derive actionable insights for operational efficiency and optimisation
• Develop frameworks to synthesize complex operational data (e.g., vehicle performance, route optimisation, and experiments scheduling) to inform strategy at both the product and company level
• Identify key performance metrics for fleet operations and continuously refine them to ensure they align with wider business goals
• Create and apply novel experimental methodologies to enhance the signal-to-noise ratio and speed up feedback loops, improving operational decision-making and optimising use of on-road testing for ML advancements
• Combine experimental methods with causal inference techniques to test and optimise operational strategies