Data Scientist (M/F/D) in Basel

• Apply established and novel statistical and computational approaches to model large-scale, multimodal clinical and biomedical datasets to identify biomarkers predictive of disease progression and treatment response, and to generate hypotheses for novel therapeutic targets.
• Integrate and analyze large-scale multi-omics datasets, including genomics, transcriptomics, proteomics, and metabolomics, to investigate disease mechanisms and support translational research and drug discovery.
• Develop, evaluate, and apply preclinical computational models using internal and publicly available datasets to support departmental research projects and generate actionable biological insights.
• Apply artificial intelligence and machine learning methods to scientific and R&D data, leveraging advanced computational approaches to investigate complex biological systems and support early drug development.
• Utilize AI tools, including large language models (LLMs), foundation models, and agentic workflows, to accelerate data analysis, automate research processes, and improve scientific decision-making.
• Collaborate closely with interdisciplinary teams of Translational Research and Data Science scientists, working at the interface of computational and experimental biology to translate complex datasets into meaningful scientific conclusions.
• Actively contribute to cross-functional drug discovery and early development projects, using data-driven insights to guide research strategies and therapeutic target identification.
• Work with clinical datasets in accordance with responsible data handling practices, ensuring scientific rigor, reproducibility, and appropriate data governance.
• Develop reproducible analytical workflows and communicate findings through high-quality visualizations, reports, and scientific presentations to internal stakeholders and external research partners.
• Contribute to collaborative research initiatives with leading healthcare institutions, with opportunities to disseminate scientific findings through publications.

Qualification

• Degree in Data Science, Bioinformatics, Computational Biology, Biostatistics, Life Sciences, Biomedical Sciences, or a related discipline, with relevant industry experience in life sciences research.
• Demonstrated working knowledge and practical application of statistics and data modeling to large-scale biomedical and life sciences datasets.
• Hands-on experience integrating and analyzing large-scale multi-omics datasets, including genomics, transcriptomics, proteomics, and metabolomics, to investigate disease biology and answer scientific research questions.
• Proven experience applying artificial intelligence and machine learning methods to scientific, biomedical, or R&D datasets.
• Practical experience working with clinical data in an industry or translational research setting, with an understanding of responsible data handling.
• Proficiency in
• R and/or Python

, with competency working in a Linux environment.

• Experience using AI tools, including large language models (LLMs) and agentic workflows, to accelerate scientific analysis and decision-making.
• Demonstrated commitment to reproducible research, including the development of robust analytical workflows, data visualization, and scientific reporting.
• Ability to translate complex computational findings into actionable biological insights and communicate results effectively to interdisciplinary stakeholders.
• Strong collaboration and problem-solving skills, with a practical, team-oriented approach and the ability to work across computational and experimental research teams.
• Excellent written and oral communication skills in English.
• Experience working with or fine-tuning foundation models for biological, chemical, or clinical applications.
• Experience with advanced computational approaches for single-cell and bulk RNA sequencing, spatial transcriptomics, proteomics, or other high-dimensional biological datasets.
• Familiarity with deep-learning methods and foundation models applied to biological and clinical data.
• A strong interest in translational research, drug discovery, and addressing unmet patient needs.
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