Data Scientist (M/F/D)

ulliApply 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. /liliIntegrate and analyze large-scale multi-omics datasets, including genomics, transcriptomics, proteomics, and metabolomics, to investigate disease mechanisms and support translational research and drug discovery. /liliDevelop, evaluate, and apply preclinical computational models using internal and publicly available datasets to support departmental research projects and generate actionable biological insights. /liliApply artificial intelligence and machine learning methods to scientific and RD data, leveraging advanced computational approaches to investigate complex biological systems and support early drug development. /liliUtilize AI tools, including large language models (LLMs), foundation models, and agentic workflows, to accelerate data analysis, automate research processes, and improve scientific decision-making. /liliCollaborate 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. /liliActively contribute to cross-functional drug discovery and early development projects, using data-driven insights to guide research strategies and therapeutic target identification. /liliWork with clinical datasets in accordance with responsible data handling practices, ensuring scientific rigor, reproducibility, and appropriate data governance. /liliDevelop reproducible analytical workflows and communicate findings through high-quality visualizations, reports, and scientific presentations to internal stakeholders and external research partners. /liliContribute to collaborative research initiatives with leading healthcare institutions, with opportunities to disseminate scientific findings through publications. /li /ulh3Qualification: /h3ulliDegree in Data Science, Bioinformatics, Computational Biology, Biostatistics, Life Sciences, Biomedical Sciences, or a related discipline, with relevant industry experience in life sciences research. /liliDemonstrated working knowledge and practical application of statistics and data modeling to large-scale biomedical and life sciences datasets. /liliHands-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. /liliProven experience applying artificial intelligence and machine learning methods to scientific, biomedical, or RD datasets. /liliPractical experience working with clinical data in an industry or translational research setting, with an understanding of responsible data handling. /liliProficiency in bR and/or Python /b, with competency working in a Linux environment. /liliExperience using AI tools, including large language models (LLMs) and agentic workflows, to accelerate scientific analysis and decision-making. /liliDemonstrated commitment to reproducible research, including the development of robust analytical workflows, data visualization, and scientific reporting. /liliAbility to translate complex computational findings into actionable biological insights and communicate results effectively to interdisciplinary stakeholders. /liliStrong collaboration and problem-solving skills, with a practical, team-oriented approach and the ability to work across computational and experimental research teams. /liliExcellent written and oral communication skills in English. /liliExperience working with or fine-tuning foundation models for biological, chemical, or clinical applications. /liliExperience with advanced computational approaches for single-cell and bulk RNA sequencing, spatial transcriptomics, proteomics, or other high-dimensional biological datasets. /liliFamiliarity with deep-learning methods and foundation models applied to biological and clinical data. /liliA strong interest in translational research, drug discovery, and addressing unmet patient needs. /li /ul #J-18808-Ljbffr