Computational Biologics Design – Associate Principal Scientist

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About the role

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.   Essential Skills/Experience: • PhD in relevant field (e.g. Structural Biology, Computer Science, Bioinformatics, Physics and Mathematics) • Knowledge of computational structural biology and demonstrated application of data analysis methods • Experience with structural modelling platforms (e.g. Schrodinger, Rosetta etc) • Familiarity with antibody discovery & optimisation, protein structures • Skilled in applying generative AI, Machine learning or deep learning to design and optimise proteins, VHH and antibodies • Strong, professional communication skills and excellent attention to detail, capable of developing good working relationships with diverse individuals • Experience working within a team environment • Acts with integrity and does the right thing   Desirable Skills/Experience: • Knowledge of FAIR data principles • Experience with the analysis of large structural, sequence and experimental datasets.   When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in

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