Data Scientist, Data Quality & Provenance

📋 Description

• Define measurable, statistically rigorous concepts of data quality, including coverage, label
• Apply statistical-inference methods to multi-rater annotation, label ambiguity, dataset coverage
• Partner with annotation, autonomy and evaluation teams to translate practical quality questions
• Build automated reports that clearly communicate confidence, limitations and appropriate
• Analyse large annotation datasets using Python and SQL to identify quality issues and inform
• Iterate on metrics and reporting based on feedback from the teams using them.

🎯 Requirements

• Strong foundations in classical statistics, including experimental design, sampling, modelling
• Practical experience with rater or annotator modelling, inter-rater agreement, label uncertainty or
• Understanding of data quality across dimensions such as coverage, label quality, uncertainty
• Experience with annotation tooling, QA methodologies, human-in-the-loop ML systems or multi-rater
• Strong Python and SQL skills and confidence analysing large, complex datasets.
• Ability to turn ambiguous stakeholder questions into rigorous, usable metrics and clearly

🎁 Benefits

• 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 with support for training, conferences and growth
• Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave