Niall Robinson
Developer Realtionship Manager For Weather And Climate
Exeter, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolNiall Robinson is a developer‑relations and innovation leader with 13 years’ experience translating weather and climate science into operational products and industry partnerships. Currently at NVIDIA and a Visiting Fellow at the University of Exeter, he blends hands‑on software contribution—such as backend improvements to the widely used SciTools/iris Earth‑science Python library—with strategic roles shaping AI, open data and cloud collaborations at the Met Office. He has led multi‑disciplinary teams that prototype novel interfaces (VR, voice), build data portals and drive open data provenance and green‑finance information products. Comfortable moving between research and production, he has also contributed to pandemic modelling efforts for policy advising and nurtured regional data‑driven SMEs, reflecting a knack for making complex geoscientific data practical and accessible.
13 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy, Atmospheric Science, Doctor of Philosophy, Atmospheric Science at The University of ManchesterThe High School of Glasgow
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
iris-go10
iri10
python10
intersystems-iris10
numpy10
data-analysis10
testing9
data-structures8
data-structure8
data-science7
pandas6
Programming languages (6)
Github contributions (5)
SciTools/iris
Apr 2013 - Aug 2018
A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
Role in this project:
Backend Developer
Backend DeveloperContributions:12 commits, 4 PRs, 27 comments in 5 years 4 months
Contributions summary:Niall primarily contributed to the Iris project by modifying and enhancing existing functionalities related to coordinate categorization and analysis. They refactored code related to coordinate categorisation, ensuring naming consistency and added functions to calculate statistical correlations between data cubes. Furthermore, they implemented features like informative error messages for uncollapsible coordinates and added methods for determining differences between cubes, improving the library's robustness. Additionally, they included masked array support for rolling windows and introduced automatic downcasting of array precision for saving, improving data handling.
pythonvisualisationagnosticgribatmosphere
informatics-lab/threejs-modata-earth
Oct 2016 - Nov 2016
Contributions:38 commits, 13 PRs, 18 pushes in 8 days
globewebglmetjavascriptthreejs
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