Data Scientist / Data Engineer
Build and audit cloud data pipelines for medical imaging and clinical datasets, apply ML/LLMs to oncology problems, and ensure regulatory-grade data quality and traceability.
- Compensation
- Not disclosed
- Location
- Berlin, Berlin, Germany
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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
- 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
Full description
About Nucs AI
Nucs AI is revolutionizing cancer care through cutting-edge AI and medical imaging technology. Founded in 2024 by a multidisciplinary team of oncologists, AI researchers, and healthcare innovators, we’re tackling one of medicine’s most pressing challenges: the growing demand for accurate, timely cancer diagnostics in the face of rising scan volumes and limited radiologist capacity.
We sit at the intersection of diagnostics and treatment planning - building AI-powered tools at the convergence of medical imaging, radioligand therapy, and artificial intelligence. Starting with prostate cancer and expanding across oncology, we partner with world-leading medical institutions and pharmaceutical companies across the US, Europe, and Australia to bring precision oncology into everyday clinical practice.
Our mission is to enhance diagnostic precision and expand access to expert-level cancer care, improving patient outcomes worldwide. We’re venture-backed, early-stage, and building a team that blends deep clinical expertise with engineering intensity - moving with the rigor the medical field demands and the speed the problem deserves.
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What you'll do
- 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.
What we're looking for
- 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.