Computational Biologist Principal Research Scientist II

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About the role

Position Overview We are seeking an accomplished scientific leader in AI-enabled computational biology to establish and advance next-generation immunogenicity and translational immunology capabilities across biologics discovery and development. The successful candidate will serve as a discipline expert and strategic partner, combining machine learning, protein language models, structural modeling, systems immunology, and computational biology to predict, understand, and mitigate anti-drug antibody (ADA) responses and other immune-mediated liabilities across therapeutic proteins, multispecific antibodies, antibody-drug conjugates, gene therapies, and novel modalities. This individual will define scientific strategy, lead high-impact cross-functional programs, build fit-for-purpose computational capabilities, and translate complex analyses into clear recommendations for project teams, governance bodies, and senior leaders. The role includes direct people leadership and accountability for developing an effective, collaborative, and scientifically excellent team. Key Responsibilities Scientific Strategy and Functional Leadership • Develop and deliver the computational immunogenicity strategy in alignment with Development Sciences priorities and key scientific objectives. • Serve as the discipline expert and primary contact for AI/ML-enabled immunogenicity, protein engineering, and translational modeling. • Identify emerging scientific and technology trends, assess their relevance, and translate them into short- and long-term functional objectives. • Create, champion, and implement innovative approaches that improve candidate selection, risk mitigation, and transition success. AI/ML-Based Immunogenicity and Protein Modeling • Lead development and deployment of predictive models for preclinical and clinical immunogenicity risk assessment. • Integrate ADA incidence, PK/PD, HLA, sequence, structure, developability, assay, and patient-level data using machine learning and deep learning approaches. • Apply protein language models, foundation models, transformers, generative AI, and physics-informed modeling to identify immunogenicity drivers, biomarkers, and design opportunities. • Develop or guide methods to predict ADA incidence and persistence, T-cell epitope content, HLA-associated risk, patient susceptibility, and drug-like properties of biologics. Systems Immunology, Multi-Omics, and Translational Modeling • Integrate transcriptomic, proteomic, single-cell, spatial biology, flow cytometry, bioanalytical, and clinical datasets to characterize immune responses to biologics. • Build computational frameworks linking in vitro and ex vivo assays, nonclinical observations, molecular mechanisms, and clinical ADA or neutralizing-antibody outcomes. • Identify biomarkers associated with immune activation, tolerance, and ADA development, and use findings to inform translational and clinical strategies. Generative AI, Knowledge Integration, and Data Platforms • Lead development of PLM- and LLM-enabled tools for scientific knowledge mining, hypothesis generation, and immunogenicity risk assessment. • Guide retrieval-augmented generation and agentic workflows that use scientific literature, historical studies, internal reports, and regulatory guidance. • Establish integrated, traceable immunogenicity data assets and scalable data pipelines spanning discovery, nonclinical, and clinical development. • Ensure analytical approaches are reproducible, scientifically defensible, appropriately documented, and fit for their intended use. Cross-Functional Influence and Communication • Collaborate with experts across Discovery, Protein Sciences, Bioanalysis, Clinical, Regulatory, Toxicology, Data Science, and other Development Sciences func

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