Data Scientist, Robotaxi Performance & Capability

🧠 Your day-to-day

You’ll take ownership of hands-on analysis, from exploring complex datasets in Python and SQL to sharing findings that guide product and engineering decisions.

• Investigate driving behaviours and operational events to understand where, why and how often the system falls short of driverless service expectations.

• Build practical metrics, performance baselines and dashboards to track progress across model, software and product releases.

• Apply statistical methods to assess rare events, quantify uncertainty and distinguish meaningful improvements from noise.

• Work with teams across Wayve to turn findings into development priorities, targeted validation and better data collection.

• Contribute to product readiness reviews with clear evidence of progress, remaining limitations and the confidence behind your conclusions.

🧩 What you’ll be working on

• Understanding driverless readiness: Assess real-world driving and validation data to identify performance gaps, quantify their operational impact and track whether they are closing.

• Separating performance gaps from missing capabilities: Identify where better model performance can help and where additional system or product capabilities are needed.

• Measuring the complete Robotaxi experience: Define metrics for remote assistance, passenger pickup and drop-off, passenger interactions, vehicle readiness and recovery from operational disruptions.

• Evaluating service reliability: Measure intervention demand, mission completion, recovery time and vehicle availability to understand what it takes to deliver a dependable driverless service.

• Exploring challenging scenarios: Investigate safety-critical interventions, unnecessary stops, stuck scenarios and manoeuvre execution across different roads and operating conditions.

🙌 You should apply if

• You have at least three years of relevant experience in data science, automotive data analysis or a related field, with deeper experience for senior-level opportunities.

• You’re hands-on with Python, SQL, statistical analysis and tools for exploring and visualising large datasets.

• You can turn an ambiguous real-world problem into measurable questions, useful KPIs and a practical analytical approach.

• You’ve analysed complex systems, uncovered performance limitations and translated your findings into product or engineering improvements.

• You understand statistical confidence, rare-event analysis, performance distributions and trends.

• You’re comfortable working independently with incomplete data, making reasonable assumptions and refining your approach as new evidence emerges.

• You communicate clearly with engineering, product and leadership stakeholders, explaining both your findings and their limitations.

Experience in autonomous driving, ADAS, robotics, mobility services or another complex physical system would be valuable. Familiarity with vehicle dynamics, driving behaviour, safety-critical events or operational reliability would also help. A relevant master’s degree or PhD in Physics, Statistics or Mathematics, particularly focused on traffic systems, autonomous driving deployment or urban mobility, is a plus.

🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.