Sr. Scientist - Computational & Applied AI

About the role

Roche's newly established Computational Sciences Center of Excellence (CS-CoE) brings together its pRED and gRED research and early development organisations to use data and artificial intelligence to accelerate drug discovery. Within this centre, the Computational Biology and Medicine team turns AI-driven hypotheses into validated therapeutic targets. The Senior Computational and Applied AI Scientist develops and deploys translational and applied AI frameworks for the Immunology and Cardiovascular, Renal, and Metabolism (CVRM) portfolios, bridging advanced algorithmic models with mechanistic validation.

Key responsibilities

• Co-develop and implement explainable and causal AI methods (causal models, symbolic regression, counterfactual inference) to map disease pathways and validate targets
• Adapt and fine-tune biological foundation models to predict novel therapeutic targets in immune diseases and CVRM
• Implement adversarial domain adaptation and cross-species translation to align animal model phenotypes with human patient endotypes
• Use patient-level multi-omics, spatial profiles and real-world clinical data to define mechanistic patient selection criteria
• Build physics-informed models such as Neural ODEs and PINNs to capture physiological kinetics and patient digital twins in silico
• Apply graph-based deep learning and build large-scale biological knowledge graphs for tissue-remodeling and auto-inflammatory networks
• Translate ML-ready frameworks into portfolio deliverables and work with clinical scientists, wet-lab experts, biostatisticians and enterprise engineers
• Ensure provenance and explainability in all insights delivered to stakeholders

Required profile

• PhD (preferred) or MSc in Computational Biology, Computer Science, Machine Learning, Bioinformatics, Systems Biology, AI or another highly quantitative field
• 3+ years (Senior Scientist) or 6+ years (Principal Scientist) of post-doctoral or biopharmaceutical industry experience delivering applied AI and data science solutions in drug discovery
• Active interest in human immunology, complex diseases or system-level pathophysiology
• Proven cross-functional collaboration between computational engineers and wet-lab experimentalists

Required skills

• Causal inference toolkits, graph neural networks or generative models on single-cell, spatial omics or clinical trial data
• Adapting large-scale biological foundation models or building dynamic systems models