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# Data Scientist, Data Quality & Provenance

- **Company:** Wayve
- **Location:** Germany (Remote available)
- **Contract:** Permanent contract
- **Skills:** Big Data, Data Integration, Python (Programming Language), Standard Sql, SQL Databases, Model Validation, AI Platforms, Machine Learning Operations, Data Selection
- **Published:** November 12, 2026
- **Apply:** https://www.adzuna.de/details/5961901050

## About the Role

* 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.

## Description

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, * 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.