Data Scientist, Data Quality & Provenance Team

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Description

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 distingu

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