Data Scientist, Data Quality & Provenance

Overview

In this role, you define and apply rigorous data-quality metrics to support trustworthy data, annotations, and model evaluation for Wayve’s autonomous-driving platform. You’ll translate practical quality questions into defensible metrics and automate reporting to communicate confidence and limitations. You will collaborate with annotation, autonomy, and evaluation teams to improve data pipelines and guide decision-making. Your work directly impacts data-driven safety and efficiency in real-world driving deployments.

Pay / Benefits

• Salaries benchmarked against market annually
• Meaningful equity and ownership
• Relocation support and visa sponsorship where applicable
• Hybrid working with core hours
• Learning and development budgets for training and conferences
• Comprehensive benefits including health insurance and parental leave

Responsibilities

• Define measurable, statistically rigorous data-quality concepts (coverage, label quality, uncertainty, provenance, model performance)
• Apply statistical-inference methods to multi-rater annotation, label ambiguity, dataset coverage and model evaluation
• Collaborate with annotation, autonomy and evaluation teams to translate quality questions into metrics
• Build automated reports that clearly communicate confidence, limitations and interpretation
• Analyse large annotation datasets with Python and SQL to identify quality issues and inform decisions
• Iterate on metrics and reporting based on user feedback
• Develop metrics for labeler/dataset quality to support training-data selection and model evaluation
• Explore multi-rater ML approaches (e.g., Dawid–Skene, inter-rater agreement) and quality standards across data dimensions
• Integrate data-science outputs into platform workflows used by engineering and research teams

Key requirements

• Strong foundations in classical statistics (experimental design, sampling, modelling, estimation and inference)
• Experience with rater/annotator modelling, inter-rater agreement, label uncertainty
• Understanding of data-quality dimensions (coverage, label quality, uncertainty, provenance, model performance)
• Experience with annotation tooling, QA methodologies, human-in-the-loop ML, or multi-rater labeled datasets
• Strong Python and SQL skills; ability to analyse large, complex datasets
• Ability to translate ambiguous stakeholder questions into rigorous, usable metrics and clearly communicate uncertainty and implications
• Bonus: experience with probabilistic or Bayesian modelling, production platform integrations, or safety-critical data

• Translating ambiguous questions into measurable metrics
• Clear communication of uncertainty and limitations
• Collaborative cross-team engagement
• Python for data analysis
• SQL for large-scale data analysis
• Statistical modelling and inference

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