Staff Data Analyst

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other basis as protected by applicable law.  

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—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 part of our mission to scale end-to-end embodied AI for autonomous driving, we are building a world-class Data Management team focused on unlocking high-quality, targeted data acquisition that drives model performance and predictability. We are looking for a highly analytical and systems-minded ODD & Behavioural Competency Analyst to lead the analysis of Operational Design Domains (ODDs), traffic patterns, and regulatory behaviours across our target markets.

This role will be critical in defining what data is needed, where, and how much, in order to build models with high Mean Time Between Failures (MTBF) and generalisation capabilities across varied geographies.

Key responsibilities

• Analyse and define the operational design domain (ODD) for each target market or region, including geography, infrastructure, weather, road types, traffic density, and local driving behaviours.
• Identify ODD boundaries, edge conditions, and failure triggers to inform data collection and system design.
• Build and maintain a taxonomy of behavioural competencies (e.g., merging, yielding, unprotected turns, interacting with pedestrians) required to safely operate in each ODD.
• Quantify the complexity and frequency of each competency based on local traffic data, regulations, and real-world observations.
• Develop a framework to compute and prioritise permutations of ODD parameters and behavioural competencies to optimise data collection, scenario coverage, and scaling efficiency.
• Recommend minimal data slices needed to support safe and predictable system performance in a new region.
• Analyse traffic laws, enforcement patterns, and common driving behaviours in new markets to assess differences in required system behaviour and edge case risks.
• Collaborate with legal and safety teams to map regulations to system-level behavioural expectations.
• Work closely with data engineering, safety, simulation, product, and deployment teams to turn ODD and competency insights into actionable data strategies and deployment plans.
• Provide input to scenario library development, synthetic data generation, and test case prioritisation.
• Use historical data and statistical models to identify data gaps or high-variance behaviours that impact MTBF performance.
• Provide guidance on what additional data is needed to reach MTBF targets in each ODD segment.

About you 

In order to set you up for success as a Data Analyst at Wayve, we’re looking for the following skills and experience.  

Essential 

• 7+ years experience in systems engineering, transportation analysis, data science, or a related field.
• Proven experience with traffic system modeling, autonomous driving deployment, or urban mobility analysis.
• Experience analysing or defining ODDs in the context of AV/ADAS technologies is a strong plus.
• Familiarity with traffic regulations and human driving behaviours across multiple geographies.
• Proficiency in data analysis tools (e.g., Python, SQL, GIS, Jupyter) and ability to visualise ODD and scenario coverage metrics.
• Ability to work cross-functionally and translate domain analysis into technical and product requirements.
• Experience working in an agile, fast-scaling environment with a strong execution mindset.

Desirable:

MS or PhD in Physics, Statistics or Mathematics or related field with specialism in traffic system modelling, autonomous driving deployment, or urban mobility analysis.

 

This is a full-time role based in either 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.   We operate core working hours so you can determine the schedule that works best for you and your team.  

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We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.

For more information visit Careers at Wayve. 

To learn more about what drives us, visit Values at Wayve 

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DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.