Data Scientist, Fleet Operations

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.

This means you might:

• 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.
  
  

• 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 summarise, visualise, 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.
  
  

• Practical experience with machine learning and optimization techniques (e.g., pytorch, scikit-learn).
  
  

• Experience promoting statistical rigor and experimental best practices in previous roles.
  
  

• Familiarity with causal inference, econometrics, or Bayesian methods for testing hypotheses in operations research.
  
  

• Prior experience working with large datasets and distributed computing (e.g., Spark, Hadoop).
  
  

• Experience in a fast-paced tech or startup environment.
  
  This is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.