Translational data scientist

Key Responsibilities

• 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.