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

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.

• 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 reporting   systems   or shared analysis packages, with confidence in version control and collaborative software - development practices.

• Demonstrable   experience building   research   data infrastructure   that enables structured, quality- controlled   and reproducible data , for example   through   LIMS schemas ,   electronic laboratory notebook workflows, structured databases   or   reproducible data pipelines with automated validation and quality control.

• Strong   proficiency   in Python and/or R, and experience of large-scale data management.

• Demonstrated ability to   support   adoption of new computational capabilities across research teams, including user engagement, documentation,   training   and communication with scientific leadership.

• Evidence of influencing scientific or technical direction beyond an immediate project team through technical leadership, best-practice development,   mentoring   or capability building.

• Strong interpersonal and collaboration skills, with   a track record   of working effectively across wet-lab and dry-lab teams in a matrixed environment.

• Experience preparing written scientific reports and delivering oral presentations .

• PhD in relevant disciplines or equivalent experience (e.g.    Software Engineering,   Computational Biology,   Machine Learning, Data Science, or related fields ).

• Experience with advanced deep learning model families (graph neural networks, transformers, probabilistic models) applied to biological data.

• Experience with data   science   platforms such as Domino   or   QuartzBio .

• Experience working with   biological datasets in immunology,   oncology   or related therapeutic areas, with the ability to rapidly gain domain knowledge as needed.

• Experience in an industry drug discovery setting, with knowledge of discovery-stage decision-making.

Our Social Media,

• Follow AstraZeneca on

: https://www.linkedin.com/company/1603/

• Follow AstraZeneca on

: https://www.facebook.com/astrazenecacareers/

• Follow AstraZeneca on