Computational Biologics Design - Associate Principal Scientist

ASTRAZENECA UK LIMITED; Hartford; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

AstraZeneca UK Limited seeks an in silico project leader to own computational strategy across multiple therapeutic programs, shaping design hypotheses and key decisions. You will co-develop scalable structural analytics workflows with discovery and data science colleagues to accelerate candidate design and create reproducible insights.

You will lead end-to-end data capability integrating structural, sequence and experimental data to actionable recommendations, evaluating next-gen computational

Qualifications

• PhD in relevant field (e.g. Structural Biology, Computer Science, Bioinformatics, Physics and Mathematics) is required.
• Experience with computational structural biology and data analysis methods.

Responsibilities

• Own the computational strategy for multiple programs and inform design hypotheses.
• Co-develop scalable, reproducible structural analytics workflows with discovery and data science colleagues.
• Lead end-to-end data capability connecting structural, sequence and experimental data to actionable recommendations.
• Evaluate and deploy new computational tools and platforms for biologics discovery and design.
• Drive AI-driven design and optimisation for proteins, VHHs and antibodies.
• Engage in external collaborations to translate science into practical tools and impact.

Job description

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., Here, your expertise fuels a bold mission to push scientific frontiers and transform outcomes in some of the toughest diseases, including cancer. You will work at the intersection of cutting-edge computation and lab discovery, alongside diverse minds who combine academic rigor with real-world delivery. Our global network spans hundreds of collaborations across many countries and includes partnerships with leading oncology centres, giving you access to breakthrough ideas, high-quality data and the chance to see your methods scale from concept to clinic. We bring surprising combinations of experts together to spark fresh thinking, value kindness alongside ambition and equip you with the technology, mentorship and remit to make a meaningful, patient-focused impact.

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.,

• 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 the office while respecting individual flexibility. Join us in our unique and ambitious world.