Data Scientist, Data Quality & Provenance

Highlights

• Build evidence that powers confident decisions for embodied AI data systems.
• Define rigorous measures of coverage, label quality and provenance.
• Create automated reports that replace one-off analyses with trusted metrics.

What they offer

• Salaries benchmarked against the market annually
• Meaningful equity sharing in long-term success
• Relocation support and visa sponsorship where applicable
• Hybrid working with core hours and hands-on workshop and lab work
• Learning and development budgets for training, conferences and growth
• Comprehensive benefits including health, dental, parental leave, pension, wellbeing support and team socials

Ideal candidate

A statistically-minded problem solver who loves turning ambiguous quality questions into trusted evidence and cares about safe autonomy. You take ownership, communicate uncertainty clearly, and thrive in an evolving, mission-driven environment.

Requirements

• Strong foundations in classical statistics including experimental design, sampling, modelling, estimation and inference
• Practical experience with rater modelling, inter-rater agreement, label uncertainty or related methods
• Understanding of data quality across coverage, label quality, uncertainty, provenance and model performance
• Experience with annotation tooling, QA methodologies, human-in-the-loop ML or multi-rater labelled datasets
• Strong Python and SQL skills with confidence analysing large, complex datasets
• Ability to turn ambiguous stakeholder questions into rigorous metrics and communicate uncertainty and implications