Computational Biologics Design – Associate Principal Scientist

Overview

In this role you will lead in silico design for biologics across oncology, respiratory and cardiovascular areas, translating structural and experimental data into actionable design insights. You will work closely with discovery and data science teams to build scalable analytics workflows and advance antibody, VHH and other protein modalities. You will evaluate and deploy new Computational capabilities and apply AI-driven methods to improve potency, specificity and developability. This position combines leadership, collaboration and hands-on delivery in a data-driven Biologics design context.

Responsibilities

• Own the computational strategy for multiple therapeutic programs and influence design decisions
• Co-develop scalable structural analytics workflows with discovery and data science colleagues
• Contribute to an integrated end-to-end data analysis capability linking structural, sequence and experimental data
• Lead evaluation and deployment of next-generation computational tools and platforms
• Drive AI-driven design and optimisation of proteins, VHHs and antibodies for potency, specificity and developability
• Collaborate with external partners to translate emerging science into practical tools
• Communicate complex concepts to non-experts and mentor colleagues on computational best practices
• Deliver immediate modelling and analytics to move programs forward and build reusable frameworks

Key requirements

• PhD in a relevant field
• Knowledge of computational structural biology and data analysis methods
• Experience with structural modelling platforms (Schrodinger, Rosetta)
• Familiarity with antibody discovery and optimisation, protein structures
• Skilled in applying generative AI, machine learning or deep learning to protein design
• Strong communication and attention to detail; ability to work with diverse teams
• Experience working within a team environment
• Integrity and ethical conduct

• strong communication
• attention to detail
• team collaboration
• computational structural biology
• data analysis methods
• structural modelling platforms (Schrodinger, Rosetta)

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