Wayve is looking to optimize fleet operations through data-driven insights and operational research to enhance the usability and safety of automated driving systems.
Requirements
- 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.
- Practical experience with machine learning and optimization techniques (e.g., pytorch, scikit-learn).
- Familiarity with causal inference, econometrics, or Bayesian methods for testing hypotheses in operations research.
Responsibilities
- 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.
Other
- 3+ years of experience in a Data Science role, with a focus on operations research, process automation and optimisation, or similar fields.
- 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.
- Experience promoting statistical rigor and experimental best practices in previous roles.
- Prior experience working with large datasets and distributed computing (e.g., Spark, Hadoop).
