Sr. Scientist - Computational & Applied AI

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Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations (gRED and pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CS-CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

Within this newly established Center of Excellence, our activities operate across a distinct continuum of innovation and explainability. The Computational Biology and Medicine team stands as the vital final link in this continuum. Focussed on our delivery for the portfolio we bridge advanced algorithmic models with deep mechanistic validation to turn AI-driven hypotheses into validated therapeutic targets and deepen our understanding of disease biology and our patients.

The Senior Computational and Applied AI Scientist within the Computational Biology and Medicine team is responsible for developing and deploying translational and applied AI frameworks to accelerate our Immunology and Cardiovascular, Renal, and Metabolism (CVRM) portfolios. Operating at the final stage of translational execution for Discovery and Early Development, you will focus on moving beyond pure statistical correlation toward a systems-level, mechanistic understanding of disease and target biology.

This position offers a trajectory of professional development aligned with scientific excellence and modern enterprise productivity frameworks ensuring our computational pipelines directly optimize R&D throughput and compress discovery cycles.

• Working across departments to co-develop and implement advanced machine learning models focused on explainable and causal AI methods (such as causal models, symbolic regression, and counterfactual inference) to map disease pathways and unlock true mechanistic target validation.
• Adaptation and fine-tune state-of-the-­art biological foundation models to predict novel therapeutic targets in complex immune diseases and CVRM.
• Bridging the translational gap by implementing adversarial domain adaptation and cross-species translation algorithms to dynamically align animal model phenotypes with human patient endotypes.
• Leveraging patient-level multi-omics, spatial profiles, and real-world clinical datasets to identify distinct disease endotypes, defining mechanistic patient selection criteria that enable clinical scientists to stratify responder vs. non-responder populations.
• Constructing dynamic representations and physics-informed models (e.g., Neural ODEs, PINNs) to capture physiological kinetics, temporal disease progression, and patient  digital twins  entirely in silico.
• Applying graph-based deep learning approaches, building and querying large-scale biological knowledge graphs to capture systems-level interactions across complex…