Andy Skelton is a principal research data scientist in the Data Science Lab at RAND Europe, where he leads the Applied AI work stream. He builds tools that help research teams search, extract, and structure information from large evidence bases, combining language models, knowledge graphs, and traditional analytics with clear traceability from findings back to sources.
A theme in Skelton's work is understanding how the agentic capabilities of frontier AI models, the kind that have already transformed software engineering, can be brought to bear on knowledge work more broadly. In practice, this means pairing techniques that package instructions and relevant context (often termed prompt and context engineering) with accessible interfaces and workflows, so that non-technical researchers can cover far more ground and focus their own time on interpretation and judgement rather than manual retrieval and synthesis.
His broader work at RAND spans open-source and structured intelligence analysis, emerging-technology horizon scanning, and technology risk assessment.
Before joining RAND, Skelton helped grow a climate-risk start-up spun out of the University of Cambridge and researched a range of global catastrophic risks. He holds a Ph.D. in environmental science and economics from Cambridge, with an earlier background in engineering and design.
Ph.D. in environmental economics, University of Cambridge; M.Phil. in engineering for sustainable development, University of Cambridge; M.Eng. in engineering, University of Durham
