Data Scientist, Data Quality, Provenance

Wayve

. Define measurable, statistically rigorous concepts of data quality, including coverage, label quality, uncertainty, provenance and model performance .

Posted 11/9/2026full-timeLeonberg • GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in statistical methods for data quality assessment, including classical statistics, experimental design, and multi-rater annotation. Proficient in Python and SQL for analyzing complex datasets and developing metrics that inform decision-making.

Highest-signal resume keywords

Statistical MethodsPython ProgrammingSQL ProficiencyData Quality AssessmentMulti-Rater Annotation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Classical StatisticsExperimental DesignSamplingModellingEstimationInferenceRater ModellingInter-Rater AgreementProbabilistic ModellingBayesian Modelling

Soft Skills

CommunicationAnalytical ThinkingProblem-Solving

Tools & Technologies

Annotation ToolingQA MethodologiesHuman-in-the-Loop ML Systems

Industry Keywords

Data QualityLabel QualityUncertaintyProvenanceModel PerformanceDataset Coverage

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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 communicating 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 user-team feedback
Develop statistical methods distinguishing genuine data-quality problems from label ambiguity, inconsistent annotation or evaluation noise
Create labeler and dataset metrics supporting training-data selection, simulation, annotation acceptance and model evaluation
Build reusable quality artefacts, registered automated metrics and reports
Work on multi-rater and human-in-the-loop ML systems, including Dawid–Skene-style models, psychometrics and inter-rater agreement
Establish quality standards spanning coverage, uncertainty, provenance, annotation quality and predictive-model performance
Integrate data-science outputs into platform workflows used by engineering and research teams

Requirements

What you’ll need

Strong foundations in classical statistics, including experimental design, sampling, modelling, estimation and inference
Practical experience with rater or annotator 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 systems or multi-rater labelled datasets
Strong Python and SQL skills
Confidence analysing large, complex datasets
Ability to turn ambiguous stakeholder questions into rigorous, usable metrics
Ability to clearly communicate uncertainty, limitations and decision implications
Experience with probabilistic or Bayesian modelling, production platform integrations or safety-critical data is an advantage
Eligibility to work in the country where applying

Benefits

Comp & perks

Meaningful equity, sharing in the ownership and long term success of Wayve
Relocation support and visa sponsorship where applicable
Hybrid working
Core hours
Chance to work hands on in vehicle workshops and labs
Learning and development budgets with support for training, conferences and growth
Health insurance
Dental benefits
Enhanced maternity and paternity leave
Retirement or pension where applicable
Access to therapists
Wellbeing partnerships
Team socials