The role
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.
What we are looking for
Essential
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.
Desired
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.