Data Scientist, Data Quality & Provenance Team

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 Data Quality & Provenance Team Our Data Quality & Provenance team sits within Wayve’s AI Platform organisation and builds the evidence that helps teams make confident decisions about the data underpinning our embodied AI systems. We develop statistically robust methods, metrics and tools to determine when data-quality, annotation and evaluation signals can be trusted—and when they cannot. 🧠 Your day-to-day • Define measurable, statistically rigorous concepts of data quality, including coverage, label quality, uncertainty, provenance and model performance. • Apply statistical-inference methods to multi-rater annotation, label ambiguity, dataset coverage and black-box model evaluation. • Partner with annotation, autonomy and evaluation teams to translate practical quality questions into defensible metrics. • Build automated reports that clearly communicate confidence, limitations and appropriate interpretation. • Analyse large annotation datasets using Python and SQL to identify quality issues and inform decisions. • Iterate on metrics and reporting based on feedback from the teams using them. 🧩 What you’ll be working on • Statistical methods for distinguishing genuine data-quality problems from label ambiguity, inconsistent annotation or evaluation noise. • Labeler and dataset metrics that support training-data selection, simulation, annotation acceptance and model evaluation. • Reusable quality artefacts that replace one-off analyses with registered, automated metrics and reports. • Multi-rater and human-in-the-loop ML systems, including approaches such as Dawid–Skene-style models, psychometrics and inter-rater agreement. • Quality standards spanning coverage, uncertainty, provenance, annotation quality and predictive-model performance. • Integration of data-science outputs into the platform workflows used by engineering and research teams 🙌 You should apply if • You have strong foundations in classical statistics, including experimental design, sampling, modelling, estimation and inference. • You have practical experience with rater or annotator modelling, inter-rater agreement, label uncertainty or related methods. • You understand data quality across dimensions such as coverage, label quality, uncertainty, provenance and model performance. • You have worked with annotation tooling, QA methodologies, human-in-the-loop ML systems or multi-rater labelled datasets. • You have strong Python and SQL skills and are confident analysing large, complex datasets. • You can turn ambiguous stakeholder questions into rigorous, usable metrics and clearly communicate uncertainty, limitations and decision implications. • Experience with probabilistic or Bayesian modelling, production platform integrations or safety-critical data would be an advantage. 🌱 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.   More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.   How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.   🔍 The Interview Process Our process is clear and respectful of your time • Initial call / recruiter screen (30 mins) • Competency interview: Python Programming (60 mins) Data Curation Domain (60 mins) • Deep-dive interview: Hiring Manager Interview (60 mins) Systems design interview (60 mins) • Final interview: Mission and values alignment (45 mins) We’ll always explain the format and work around your availability.   What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets