Job Title: Computational Biologics Design – Associate Principal Scientist

Location: Cambridge

Salary: Competitive

Introduction to role

Are you ready to harness AI and computational structural biology to shape the next generation of biologics that change patient outcomes? In this role, you will lead in silico design for biologic therapeutics across oncology, respiratory and cardiovascular disease areas, turning complex structural and experimental data into decisive insights that advance our pipeline.

You will join a growing, highly collaborative group focused on data-driven biologics design. Working side by side with discovery and data scientists, you will build robust structural analytics workflows, integrate new computational capabilities and help set the standard for how we design, optimise and select antibodies, VHHs and other protein modalities. Where will you take the science next?

Accountabilities

• In Silico Project Leadership: Own the computational strategy for multiple therapeutic programs, partnering with project leads to inform design hypotheses, prioritise constructs and influence key decisions.
• Structural Analytics Workflows: Co-develop scalable, reproducible structural analytics workflows with discovery and data science colleagues, accelerating candidate design and optimisation.
• End-to-End Data Capability: Contribute to an integrated, end-to-end data analysis capability that connects structural, sequence and experimental data to actionable recommendations for project teams.
• New Computational Capabilities: Lead the evaluation, integration and deployment of next-generation computational tools and platforms to enhance biologics discovery and design.
• AI-Driven Design and Optimisation: Drive innovative structural, generative and machine learning methods to design and optimise proteins, VHHs and antibodies for potency, specificity and developability.
• External Collaborations: Participate in and shape strategic collaborations with external partners, translating emerging science into practical tools and impact for our programs.
• Scientific Communication and Influence: Communicate complex concepts clearly to non-experts, present at internal and external meetings and mentor colleagues to elevate computational best practices.
• Impact Progression: Deliver immediate modelling and analytics that move current programs forward; over time, establish reusable frameworks and capabilities that uplift multiple modalities and therapy areas.