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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.

June Racchi

Gi Life Sciences Basel

Translational biomarker data scientist (multi-omics) Maternity cover 80%