3 дня назад
Data Quality Scientist (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Quality Scientist (AI): Developing statistically rigorous metrics and automated reports for evaluating data quality, annotation reliability, provenance, and model performance in autonomous-driving AI with an accent on multi-rater annotation, uncertainty, and dataset coverage. Focus on analysing large annotation datasets with Python and SQL, distinguishing genuine quality problems from evaluation noise, and integrating reusable quality metrics into engineering and research workflows.
Location: Leonberg, Germany. Hybrid working model combining in-person collaboration in the office with remote work.
Company
is building an AI platform for autonomous driving using embodied AI that learns from real-world driving experience and supports scalable vehicle deployment.
What you will do
- Define statistically rigorous concepts and metrics for data quality, annotation quality, uncertainty, provenance, coverage, and model performance.
- Apply statistical inference to multi-rater annotation, label ambiguity, dataset coverage, and black-box model evaluation.
- Partner with annotation, autonomy, and evaluation teams to turn practical quality questions into defensible metrics.
- Build automated reports that communicate confidence, limitations, and appropriate interpretation.
- Analyse large annotation datasets using Python and SQL to identify quality issues and inform decisions.
- Integrate reusable data-science outputs into platform workflows for engineering and research teams.
Requirements
- 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 predictive-model performance.
- Experience with annotation tooling, QA methodologies, human-in-the-loop ML systems, or multi-rater labelled datasets.
- Strong Python and SQL skills with confidence analysing large, complex datasets.
- Ability to translate ambiguous stakeholder questions into rigorous metrics and clearly communicate uncertainty, limitations, and decision implications.
Nice to have
- Experience with probabilistic or Bayesian modelling.
- Experience integrating data-science outputs into production platforms.
- Experience with safety-critical data.
Culture & Benefits
- Hybrid working with core hours and opportunities to work in vehicle workshops and labs.
- Market-benchmarked salaries and meaningful equity.
- Relocation support and visa sponsorship where applicable.
- Learning and development budgets for training, conferences, and professional growth.
- Benefits may include health insurance, dental care, enhanced parental leave, retirement or pension contributions, therapy access, wellbeing partnerships, and team socials.
Hiring process
- Initial recruiter call lasting 30 minutes.
- Competency interviews covering Python programming and data curation.
- Deep-dive interviews covering hiring management and systems design, followed by a final mission and values interview.
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