As a Senior Principal Computational and Applied AI Scientist, you will develop and deploy advanced AI frameworks to accelerate drug discovery and early development, bridging algorithmic models with mechanistic validation, with a focus on immunology and cardiovascular disease. Roche values personal expression, open dialogue, and genuine connections, allowing you to thrive both personally and professionally as part of our mission to prevent, stop, and cure diseases and ensure access to healthcare for all.
Tasks
- Develop and deploy translational and applied AI frameworks to accelerate Immunology and Cardiovascular, Renal, and Metabolism (CVRM) portfolios.
- Move beyond statistical correlation toward systems-level, mechanistic understanding of disease and target biology.
- 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 enable mechanistic target validation.
- Adapt and fine-tune state-of-the-art biological foundation models to predict novel therapeutic targets in complex immune diseases and CVRM.
- Implement adversarial domain adaptation and cross-species translation algorithms to align animal model phenotypes with human patient endotypes.
- Leverage patient-level multi-omics, spatial profiles, and real-world clinical datasets to identify disease endotypes and define mechanistic patient selection criteria for clinical stratification.
- Construct dynamic representations and physics-informed models (e.g., Neural ODEs, PINNs) to capture physiological kinetics, temporal disease progression, and patient digital twins in silico.
- Apply graph-based deep learning approaches and build large-scale biological knowledge graphs to capture systems-level interactions across tissue-remodeling and auto-inflammatory networks.
- Translate algorithmic innovations and ML-ready frameworks into high-value portfolio deliverables.
- Collaborate cross-functionally with clinical scientists, wet-lab disease biology experts, biostatisticians, and enterprise engineers to convert therapeutic questions into computational pipelines.
- Ensure provenance and explainability are integral features of insight delivery to stakeholders.
Requirements
- PhD (preferred) or MSc in Computational Biology, Computer Science, Machine Learning, Bioinformatics, Systems Biology, Artificial Intelligence, or a 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 a drug-discovery setting.
- 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).
- Experience adapting large-scale biological foundation models or building dynamic systems models to capture temporal disease progression.
- Active interest or deep domain understanding of human immunology, complex and multifactorial diseases, or system-level pathophysiology.
- Elite communication and teamwork skills, with a proven track record of acting as a cross-functional catalyst between computational engineers and wet-lab experimentalists.
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Über uns
Das Team
The Computational Sciences Center of Excellence (CS-CoE) is a unified group focused on harnessing data and AI to assist scientists in delivering innovative medicines. The Computational Biology and Medicine team bridges advanced algorithmic models with deep mechanistic validation, turning AI-driven hypotheses into validated therapeutic targets and deepening understanding of disease biology and patients. Collaboration across departments and with clinical scientists, wet-lab experts, biostatisticians, and engineers is central to the team's approach.