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Data Scientist – Translational Research (M/F/D)
Tasks:
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.
Desirable qualifications:
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.
Requirements:
Start: November 2026
Duration: 6 months (maternity cover)
Capacity: 80% workload
Location: Basel, Switzerland, on-site
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