Associate Principal Scientist, AI and Computational Tools, Oncology R&D (1-year FTC)

Associate Principal Scientist, AI and Computational Tools, Oncology R&D Contract: 1-year fixed-term contract Location: Cambridge, UK . Introduction to the Role: At AstraZeneca, we turn ideas into life changing medicines. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. We’re passionate about the potential of science to address the unmet needs of patients around the world. We commit to those areas where we believe we can really change the course of medicine and bring big new ideas to life. About the Role: We are seeking a highly motivated, independent and collaborative Associate Principal Scientist to join our Immune Cell Engagers Discovery group in Cambridge, UK , on a 1-year fixed-term contract. This role sits at the intersection of immuno-oncology biology, computational data science and applied AI, and is central to how we build and embed AI-first workflows across our discovery group. You will combine scientific domain expertise with strong software and data - engineering skills to lead the design and deployment of AI-powered tools, shape robust data - infrastructure strategies and serve as a recognised AI Architect for the group. You will provide technical leadership across multiple initiatives , identify opportunities, propose solutions and build capabilities that can be adopted more widely across Oncology R&D . Working closely with wet-lab scientists, data science teams and R&D IT, you will translate experimental data into scalable, reproducible and insight-generating systems , while supporting colleagues to adopt AI -enabled approaches and strong data practices. Main Duties and Responsibilities In this computational role within the Immune Cell Engagers Discovery group, you will: Lead the design, development, deployment and lifecycle management of AI-powered tools and workflows, including data - wrangling pipelines, visualisation applications, agentic AI solutions and LLM-integrated tools. Ensure solutions are maintainable, adopted by users and deliver measurable scientific value. Lead the development and evolution of data infrastructure and data standards for the discovery group , with the intended outcome of structured, quality- controlled and reproducible data that are ready for analysis and AI applications . Act as an AI Architect and technical subject matter expert for the department, defining best practices, guiding technology choices, influencing AI strategy and driving adoption of reusable code, packages and tools across teams. Identify , prioritise and lead delivery of AI and computational capability projects that address strategic scientific challenges, balancing innovation, technical feasibility, governance, sustainability and user adoption . Mentor and support colleagues in adopting AI-enabled approaches , reproducible data workflows and practical coding practices . Lead cross-functional collaborations with Data Science, R&D IT and platform teams to deliver scalable solutions, align technical and scientific standards, and influence broader computational capabilities across Oncology R&D. Develop and apply agentic workflows to extract biological insight from high-dimensional datasets, including single-cell and spatial transcriptomics, functional screening data and multiomic integration. Stay current with advances in computational biology and AI methods, tools and best practices. Proactively evaluate and adopt fit-for-purpose approaches that strengthen discovery workflows. Prepare and deliver clear scientific and technical presentations within the Immune Cell Engagers Discovery group, across Oncology R&D and to relevant leadership audiences. Ensure compliance with internal standards and external regulations, and maintain accurate and timely records in the electronic laboratory notebook. Essential Requirements Demonstrated experience leading complex computational or AI initiatives from concept through implementation, deployment and adoption within a scientific environment. Demonstrable experience using agentic AI frameworks, LLM integration or AI-assisted coding tools such as GitHub Copilot, Claude Code or similar in a research or production context. Demonstrable experience developing and deploying tools for use by others, such as Shiny applications, automated rep