Data Scientist (M/F/D)

K-Recruiting Life Sciences; Basel; On-site; CHF 110,000 - 180,000; Full time

Job summary

K-Recruiting Life Sciences seeks a data science specialist to advance translational research in Basel. You will apply statistical and computational methods to multimodal clinical datasets, integrate multi-omics, and develop preclinical models to generate actionable insights.

The role requires strong R/Python, Linux, and ML experience, with a track record in biomedical data and collaboration across interdisciplinary teams.

Qualifications

• Degree in Data Science, Bioinformatics, Computational Biology, Biostatistics, Life Sciences, Biomedical Sciences, or related discipline.
• Practical knowledge of statistics and data modeling on large-scale biomedical datasets.
• Experience integrating multi-omics datasets (genomics, transcriptomics, proteomics, metabolomics).
• Proven experience applying AI/ML to biomedical/R&D datasets.
• Experience with clinical data in industry or translational research with responsible data handling.
• Excellent English written and spoken communication.
• Experience fine-tuning foundation models for biological/clinical applications.
• Familiarity with deep-learning methods and foundation models in biology/clinical data.
• Strong collaboration and cross-functional teamwork.

Responsibilities

• Apply statistical and computational approaches to model large-scale biomedical datasets to identify biomarkers.
• Integrate multi-omics datasets to investigate disease mechanisms and support translational research.
• Develop computational models using internal/public data to generate actionable biological insights.
• Apply AI/ML to scientific, biomedical, or R&D data to support early drug development.
• Leverage AI tools, including LLMs and agentic workflows, to accelerate data analysis.
• Collaborate with Translational Research and Data Science teams to translate complex datasets into insights.
• Contribute to cross-functional drug discovery projects with data-driven strategies.
• Handle clinical datasets with responsible data governance and reproducibility.
• Communicate findings through high-quality visualizations, reports, and presentations.
• Disseminate findings via publications in collaboration with external partners.

Skills

R / Python

Linux

Machine learning

AI tools (LLMs)

Foundation models

Single-cell RNA-seq

Spatial transcriptomics

Data integration

English communication

Team collaboration

Education

Degree in Data Science, Bioinformatics, Computational Biology, Biostatistics, Life Sciences, Biomedical Sciences or related discipline

Tools

R

Python

Linux

Git

Jupyter

Docker

Kubernetes

Job description

• 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.