I am a long-term supporter and advocate for open source and open science. I work at the intersection of technology, research and data.

My work combines technical development and problem-solving with training, documentation, community engagement, and helping people make effective use of research software and infrastructure. I particularly enjoy taking something technically complex and making it understandable, usable, reproducible, and useful to others.

Developer and user advocacy — understanding user needs, testing technical services, improving developer experience, and translating feedback into practical improvements.

Technical training — designing and delivering hands-on training for researchers and technical users, particularly around Jupyter, Python, R, reproducible research, APIs, and research computing.

Community and open science — supporting research and developer communities, contributing to open-source and open-science initiatives, and connecting technical teams with the people who use their tools.

Data and analytics — working with research data, Python and R, data analysis workflows, visualisation, APIs, and reproducible computational environments.

Technical documentation — creating tutorials, learning materials, examples, documentation, and guidance that help users move from "I don't know where to start" to working independently.