Data Scientist in Oncology

Data Scientist in Oncology

University of Basel

Basel, Switzerland

We are seeking a highly motivated Data Scientist to join our multidisciplinary team at the interface of clinical research, biomedical imaging and personalized oncology. This position offers the opportunity to contribute directly to translational science and personalized cancer treatment decisions. You will be embedded in a dynamic, interdisciplinary team combining clinicians, cancer biologists, organoid and image mass cytometry experts, bioinformaticians, and data scientists. We use state-of-the-art technologies such as image-based drug screening and imaging mass cytometry to inform clinical decision making for patients with advanced cancer.

Your position

The successful candidate will play a central role in analyzing complex, image-based datasets to generate clinically actionable insights. Your main responsibilities include:

• Process and analyze image-based drug screening and imaging mass cytometry (IMC) data.
• Engineer software solutions and manage the clinical data pipeline.
• Apply advanced image analysis techniques, including segmentation, feature extraction, and quantification of cellular phenotypes and spatial patterns.
• Develop and implement AI/ML models (e.g., deep learning approaches for segmentation, classification, or predictive modeling) tailored to biomedical imaging data.
• Integrate multi-modal data to identify biomarkers and inform treatment decisions in a translational context.
• Create and maintain custom scripts and pipelines to automate and streamline data processing workflows.
• Summarize key findings in concise, actionable reports for clinical collaborators.
• Collaborate closely with, biologists, and other data experts to interpret results and refine analyses.
• Ensure rigorous data quality control, reproducibility, and critical evaluation of results.

Your profile

• PhD degree or equivalent expertise in data science, bioinformatics, computational biology, computer science, physics, engineering, or a related quantitative discipline.
• Strong programming skills in R and Python (preferred) with experience in data analysis and scripting.
• Solid knowledge of statistical methods and data visualization.
• Experience working with high-performance computing environments and Git for collaborative development.
• Experience deploying AI/ML models in production or research environments, and with deep learning frameworks such as PyTorch or TensorFlow.
• Strong team spirit, excellent communication skills (English), and ability to work in a multidisciplinary environment.
• High motivation, critical and meticulous approach to data handling and interpretation.
• Genuine interest in translational science and bridging computational methods with clinical applications.
• Background in cancer biology or oncology research is a plus. Experience in software development is a plus. Experience with analysis of sequencing data is a plus. Experience with job scheduler (e.g., SLURM) is a plus.