Senior Data Scientist

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Product Dependability Teams Our Product Dependability team connects Wayve’s embodied AI technology with the demands of real-world automotive products. Working across systems engineering, safety, cybersecurity and data analysis, the team helps translate AI capabilities into dependable vehicle-level behavior and supports integration with our OEM partners. As a Senior Data Scientist within this team, you’ll use data to understand how our driving models perform, where their limitations lie, and whether our evaluation methods reflect real-world customer needs. You’ll help define data collection and validation requirements, identify gaps or bias in datasets, and turn your findings into actionable recommendations for model development and product improvement. You’ll collaborate closely with model developers, engineering, validation and product teams. This isn’t a model-building role—it’s an opportunity to influence what we develop, how we evaluate it, and the evidence we use to demonstrate dependable performance.⁠ 🧠 Your day-to-day Analyze target markets to understand road infrastructure, weather, traffic density, local driving behaviors and regulatory requirements. Define ODD boundaries, edge conditions and potential failure triggers to inform data collection and system design. Map the driving competencies required in each region, quantifying their complexity and frequency using traffic data and real-world observations. Analyze model performance metrics and behavioral patterns to identify data gaps, edge-case risks and opportunities for improvement. Use historical data and statistical models to understand behaviors affecting reliability and Mean Time Between Failures (MTBF). Translate your findings into actionable data strategies, technical requirements and deployment recommendations with cross-functional partners. 🧩 What you’ll be working on ODD characterization: Build a detailed understanding of the environments our systems need to operate in, including geography, infrastructure, road types, weather and local traffic conditions. Behavioral competency mapping: Develop and maintain a taxonomy of capabilities such as merging, yielding, unprotected turns and pedestrian interactions, connecting each competency to the demands of its ODD. Scenario coverage and scaling: Create a framework to prioritize combinations of ODD conditions and behavioral competencies, optimizing data collection and identifying the minimum data slices needed to support safe, predictable performance in new regions. Model behavior analysis: Investigate model KPIs and driving patterns, working with safety and product teams to assess differences between