Sr. Scientist - Computational & Applied AI

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 tissue-remodeling and auto-inflammatory networks. Anchoring the final stage of the organizational continuum, translating algorithmic innovations and "ML-ready" frameworks directly into high-value portfolio deliverables. Collaborating cross-functionally with clinical scientists, wet-lab disease biology experts, biostatisticians, and enterprise engineers across pRED and gRED to convert ambiguous therapeutic questions into concrete computational pipelines. Ensuring provenance and explainability is an integral feature of your insight delivery to stakeholders

Roche’s Research and Early Development organisations (gRED and pRED) have demonstrated how AI, data, and computational sciences accelerate R&D, leveraging data and novel computational models to drive impact. The Computational Sciences Center of Excellence (CS-CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Computational Biology and Medicine team is the vital final link in a continuum of innovation and explainability. Focused on delivery for the portfolio, the team bridges advanced algorithmic models with deep mechanistic validation to turn AI-driven hypotheses into validated therapeutic targets and deepen understanding of disease biology and patients.

Roche has more than 100’000 employees across the globe, dedicated to advancing science and ensuring everyone has access to healthcare today and for generations to come. Its efforts result in more than 26 million people treated with its medicines and over 30 billion tests conducted using its Diagnostics products.

• You hold a PhD (preferred) or MSc in Computational Biology, Computer Science, Machine Learning, Bioinformatics, Systems Biology, Artificial Intelligence, or a highly quantitative field.

• You bring 3+ years (Senior Scientist) or 6+ years (Principal Scientist) of post-doctoral or biopharmaceutical industry experience delivering applied AI and data science solutions in a drug-discovery setting.

• You possess hands-on experience applying causal inference toolkits, graph neural networks, or generative models to high-dimensional biomedical data (single-cell, spatial omics, or clinical trial datasets).

• You possess experience adapting large-scale biological foundation models or building dynamic systems models to capture temporal disease progression.

• You demonstrate an active interest or deep domain understanding of human immunology, complex and multifactorial diseases, or system-level pathophysiology.

• You exhibit elite communication and teamwork skills, with a proven track record of acting as a cross-functional catalyst between pure computational engineers and wet-lab experimentalists.