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# Data Scientist / Data Engineer

- **Company:** Nucs Ai
- **Location:** Berlin, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Automation of Tests, Cloud Database, Code Review, Continuous Integration, Information Engineering, Data Systems, Dicom, Graph Database, Python (Programming Language), Machine Learning, SQL Databases, Large Language Models, Deep Learning, Model Validation, Git Flow, Data Lineage
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/data-scientist-data-engineer-nucs-ai-8963465

## About the Role

* A master's degree or PhD in computer science, data science, engineering, mathematics, statistics, physics, economics or a related discipline
 * 3+ years of hands-on experience in data engineering or a closely related field, with demonstrated development of production-grade data solutions Proficiency in SQL and Python and software-development standards such as Git-based workflows, automated testing, code reviews and CI/CD
 * Comfort working with medical imaging formats including DICOM and NIfTI.
 * Practical experience with classical machine learning and explainable ML.
 * Working fluency with deep learning and modern LLM tooling.
 * Experience with cloud data infrastructure. We use GCP, but equivalent platforms are fine.
 * A strong working understanding of data lineage, provenance, traceability, and reproducibility.
 * Unusual attention to detail around data completeness, validity, consistency, and edge cases.
 * Flexibility at adapting to new technologies
 * Awareness of good software practices, * Experience with medical imaging -clinical trials, pharmaceutical, healthcare or another regulated industry is an advantage
 * Experience with knowledge graphs / temporal knowledge graphs
 * Experience producing statistical analyses or technical evidence used in a regulatory submission.
 * Experience with model interpretability techniques such as SHAP.

## Description

* Build auditable data pipelines for large clinical, diagnostic, and medical imaging datasets in the cloud.
 * Own statistical analysis across clinical studies, diagnostic performance, model evaluation, and data quality.
 * Apply classical machine learning and deep learning methods to data analysis problems.
 * Automated ontology induction from medical documents using large language models.
 * Build dashboards and data observability tooling.
 * Maintain strong data lineage, provenance, traceability, and reproducibility across data workflows.
 * Produce analyses and supporting evidence that hold up in regulatory submissions and audits.


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