Job Description

Job Description

Position Summary Antibody AIDD (Principal Scientist), is responsible for leading a computational science team focused on biologics discovery. This role will provide critical support for the discovery and engineering of antibodies, fusion proteins, and other large-molecule therapeutics by applying and developing cutting-edge computational biology, structural biology, and AI technologies. The leader must be a technical expert capable of leading a team and collaborating closely with experimental teams in antibody engineering and protein sciences to solve core challenges in affinity, specificity, stability, and developability.

Key Responsibilities

• Team Leadership & Management: Lead and develop a team of 2-4 computational biologists, fostering a scientifically rigorous and collaborative work environment.
• Project Scientific Leadership: Serve as the core computational lead for biologics projects, directing the formulation and execution of computational strategies, including computational antigen design, antibody/protein design and optimization, and epitope prediction.
• Developability Assessment: Lead the team in establishing and applying high-throughput computational predictive models to systematically assess and optimize the developability of candidates (e.g., immunogenicity, aggregation, viscosity) at early discovery stages to de-risk downstream development.
• Technology Innovation & Platform Development: Track the latest advances in AI for biologics design; lead the development and validation of new algorithms and workflows for protein design, structure prediction, and property prediction, and promote their deployment on internal platforms.
• Cross-Functional Collaboration: Collaborate closely with teams in Antibody Discovery, Protein Sciences, Bioanalytics, and Formulation Development to form an efficient "design-build-test-learn" R&D cycle.

Basic Qualifications

• Ph.D. in Computational Biology, machine learning, Structural Biology, Biophysics, or a related field.
• 5+ years of experience in biologics R&D within the pharmaceutical or biotechnology industry.
• Excellent programming skills in Python or R and experience with relevant bioinformatics and structural biology software.
• A solid theoretical and practical foundation in protein structure modeling and molecular dynamics simulations.

Preferred Qualifications

• Structural Modeling & De Novo Design:
• Deep expertise in protein structure prediction (e.g., AlphaFold2/Multimer ), protein-protein docking, and loop modeling.
• Extensive experience using computational protein design platforms like Rosetta for de novo design, stability engineering, and binder design.
• Biophysics Simulation: Advanced knowledge and practical experience in running and analyzing all-atom molecular dynamics (MD) simulations of complex biologics (e.g., antibodies, bispecifics) to assess dynamics, stability, and aggregation propensity.
• AI for Biologics:
• Familiarity and hands-on experience with modern AI methods, such as Protein Language Models (e.g., ESMFold, ProGen) .
• Experience applying diffusion- or generative models for protein sequence and structure design.
• Knowledge of Graph Neural Networks (GNNs) for protein function prediction or developability assessment.
• Bioinformatics & Multi-Omics Integration: Proficiency in advanced sequence analysis, structural bioinformatics, and experience integrating multi-omics data (e.g., genomics, proteomics) for target identification and validation.
• Breadth & Depth of Project Experience:
• A proven track record of leading computational efforts for antibody de novo design and affinity maturation .
• Verifiable contributions to solving specific developability issues for multiple biologic formats (e.g., mAbs, VHHs, bispecifics, ADCs ).

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