Stephen Moseley
Science Manager Of Post Processing Team at Met Office
Exeter, England, United Kingdom
Summary
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Senior🎓
Top SchoolStephen Moseley is a science manager at the Met Office with nine years of professional experience focused on gridded post-processing and nowcasting, leading the IMPROVER post processing programme that transforms raw NWP output into operational weather products used by Met Office meteorologists. He manages a cross-functional team of scientists and scientific software engineers, applying Agile practices to develop, lifecycle and deliver mission-critical post processing systems that feed national forecasting capability. Stephen bridges stakeholder needs across the organisation and external partners to ensure diverse prediction requirements are met, and his work directly improves operational decision-making and national weather data quality. Trained in meteorology at the University of Reading, he combines deep domain expertise with hands-on systems stewardship and a practical focus on turning research outputs into reliable operational software. An underappreciated strength is his knack for coordinating science- and software-focused teams to keep complex pipelines robust under live operational demands.
8 years of coding experience
24 years of employment as a software developer
Bachelor of Science (BSc) Meteorology, Bachelor of Science (BSc) Meteorology at University of ReadingA-Level Maths Further Maths Physics Chemistry, A-Level Maths Further Maths Physics Chemistry at Kings School, Peterborough
Github Skills (19)
meteorology10
atmosphere10
python10
grib10
weather10
post-processing10
earth10
noaa10
earth-science10
oceanography10
netcdf10
atmospheric-modelling9
visualization9
data-analysis9
science9
Programming languages (1)
Github contributions (5)
MoseleyS/improver
Jul 2017 - Oct 2024
Contributions:2 reviews, 6 PRs, 827 pushes in 7 years 4 months
MoseleyS/iris
Dec 2019 - Aug 2021
A powerful, easy to use, and community-driven Python library for analysing and visualising meteorological and oceanographic data sets.
Contributions:2 PRs, 52 pushes, 5 branches in 1 year 8 months
python-librarymeteorologypythondata-setscensus
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