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# Data Scientist / Data Engineer

- **Company:** Nucs AI
- **Location:** München, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Cloud Database, Information Engineering, Dicom, Machine Learning, Diagnostic Tools, Large Language Models, Deep Learning, Model Validation, Data Lineage, Data Pipelines
- **Published:** August 8, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=6cc936447d88d1a4

## About the Role

* 3+ years of relevant full-time experience in data science, data engineering, medical AI, medical imaging, or a closely related field.
 * Strong statistical depth applied to messy clinical or diagnostic data.
 * A track record of building and maintaining data pipelines at scale.
 * 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

Strongly preferred

 * Experience with imaging-based clinical trials, preferably in nuclear medicine.
 * Experience supporting FDA-cleared medical devices or EU MDR compliance.
 * Experience producing statistical analyses or technical evidence used in a regulatory submission.
 * Experience with DICOM de-identification and anonymization workflows.
 * 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.
 * Use LLM-based methods to extract structured data from clinical documents and reports.
 * Build dashboards and data observability tooling.
 * Investigate and document missing, inconsistent, invalid, and implausible data.
 * 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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