Senior Scientist - AI and Computational Tools, Oncology R&D

We are seeking a highly motivated, independent, collaborative Senior Scientist to join our Immune Cell Engagers Discovery group in Cambridge, UK. This role sits at the intersection of applied AI, computational data science, and immuno‑oncology biology, and is central to how we build and embed AI‑first workflows across our discovery department. You will combine strong programming and data engineering skills with immunology domain expertise to design and deploy AI‑powered tools, build robust data infrastructure, and act as a key AI Architect for the group.

You will work closely with wet‑lab scientists, data science teams, and R&D IT to translate experimental data into scalable, reproducible, and insight‑generating systems. Critically, you will mentor and upskill colleagues across the department, helping to embed AI fluency at every level of the team.

• Design, develop, and deploy AI‑powered tools and workflows for the discovery group, including data wrangling pipelines, visualisation apps, agentic AI solutions, and LLM‑integrated tools that accelerate experimental decision‑making and reduce manual analytical burden.
• Build and maintain data infrastructure to centralise, standardise and quality control experimental data from functional cell biology assays and multi‑omic platforms, design and implement LIMS schemas, electronic lab notebook (ELN) workflows, and structured databases to ensure data is FAIR, reproducible and downstream‑ready.
• Act as an AI Architect for the department – defining best practices for AI tool use, contributing to department‑wide AI strategy, and building reusable code, packages, and tools that can be adopted broadly by the team.
• Mentor and train colleagues in computational methods, AI tool use, and reproducible data practices.
• Interface with Data Science, R&D IT, and external platform teams to co‑develop scalable solutions, contribute to shared infrastructure and ensure tools meet both scientific and engineering standards.
• Develop and apply ML and statistical models to extract biological insight from high‑dimensional datasets, including single‑cell and spatial transcriptomics, functional screening data, and multi‑omic integration.
• Contribute to the generation, understanding, and assessment of biological datasets related to functional cell biology assays and multi‑omic data to generate insights relevant to drug development in the Immune Cell Engagers Discovery group.
• Stay up to date with advances in computational biology and AI (methods, tools, and best practices); proactively evaluate and adopt fit‑for‑purpose approaches to enhance discovery workflows.
• Prepare and deliver presentations within the Immune Cell Engagers Discovery group and across other functions.
• Ensure compliance with internal standards and external regulations; maintain accurate, timely records in the ELN.

• Proven experience building data infrastructure in a research setting – including designing LIMS schemas or ELN workflow, building structural databases and developing reproducible data pipelines with automated validation and QC.
• Familiarity with agentic AI frameworks, LM integration, or AI‑assisted coding tools (e.g. Git Hub, Copilot, Claude Code, or similar) in a research or production context.
• Hands‑on experience developing and deploying tools for use by others – such as Shiny apps, automated reporting systems, or shared analysis packages; comfort with version control (Git/Git Hub) and collaborative software development practices.
• Strong proficiency in Python and/or R, and large‑scale data management.
• Strong immunology and/or immuno‑oncology domain expertise – a working understanding of immune cell biology, T‑cell function and the tumour microenvironment sufficient to independently interpret experimental data and contribute meaningfully to biological discussions.
• Experience with single‑cell and spatial transcriptomics, bulk RNA‑seq, and/or functional screening datasets; ability to build robust analytical pipelines and/or QC processes. Demonstrated ability to train users and drive adoption of new data systems or tools across research teams, including writing documentation and presenting to…