At a Glance
- Tasks: Optimise fleet operations using data-driven insights and innovative research methods.
- Company: Wayve, a leading developer of Embodied AI technology for automated driving.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Join a dynamic team in a fast-paced tech environment in London.
- Why this job: Make a real impact in the future of autonomous driving with cutting-edge AI technology.
- Qualifications: 3+ years in Data Science, strong SQL skills, and experience in operational research.
The predicted salary is between 60000 - 80000 £ per year.
About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.
In our fast-paced environment big problems ignite us—\u2014 we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
Make Wayve the experience that defines your career!
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 optimise 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 prioritisation and optimisation strategies.
Desired
- Practical experience with machine learning and optimisation 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
