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Translational biomarker data scientist (multi-omics) Maternity cover 80%
Quick overview
SeniorityMid Senior
Employment typeFull Time
Work modeHybrid
LocationBasel, Basel-Stadt, Switzerland
PythonMachine LearningLinux
Job description
Key Responsibilities
- Apply established and novel approaches to model multi-modal clinical data to identify biomarkers predictive of disease progression and treatment response, and to nominate novel therapeutic target hypotheses.
- Develop and apply a portfolio of pre-clinical models derived from internal and public datasets to support departmental projects.
- Work closely with a core team of Translational Research and Data Science scientists across wet- and dry-lab disciplines.
- Collaborate on interdisciplinary teams to model biology and drive impactful decision-making in drug discovery and early development.
- Participate in collaborations with external partners at leading healthcare institutions, with opportunities to publish scientific findings.
Essential Requirements
- Life sciences industry experience modeling biomedical research data.
- Demonstrated experience applying statistics and data modeling to large-scale life sciences datasets.
- Hands-on experience integrating multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) to answer key scientific questions and advance disease understanding.
- Industry experience working with clinical data and ensuring its responsible handling.
- Hands-on experience applying AI and machine learning methods to scientific or R&D datasets.
- Experience using AI tools, including large language models (LLMs) and agentic workflows, to accelerate analysis and decision-making.
- Proficiency in R and/or Python and competency working in a Linux environment.
- Strong commitment to reproducible research, with demonstrated visualization and reporting capabilities.
- Team-oriented mindset with a practical, problem-solving approach.
Desirable Requirements
- Experience working with or fine-tuning foundation models in biology, chemistry, or clinical data.
- Commitment to addressing unmet patient needs.
- Ability to align work with departmental priorities and scientific objectives.
- Excellent written and verbal communication skills.
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