Stephen Haddad
Scientific Systems Lead For Data Science Expertise And Guidance
Exeter, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolStephen Haddad is a Scientific Systems Lead with nine years’ experience applying software engineering and data science to weather and climate problems at the Met Office. He blends hands-on development in Python, Fortran and C++ with leadership of community training, tooling and best-practice initiatives that help scientists adopt machine learning and big-data workflows. His background includes building HPC-ready climate post‑processing pipelines, cloud-based visualization tools, and ML pilots to reduce uncertainty in ocean temperature datasets. He began his career developing 3D modeling and image-processing software for engineering applications and holds an MSc in Electrical Engineering focusing on image processing and pattern recognition. Stephen excels at turning research prototypes into robust, production-capable systems at the interface of science and commercial product development. Colleagues rely on him not only for technical depth but for pragmatic tooling and training that scale data science across organisations.
9 years of coding experience
19 years of employment as a software developer
MSc, Electrical Eningeering, Image Processing, Computer Vision, Pattern Recognition, MSc, Electrical Eningeering, Image Processing, Computer Vision, Pattern Recognition at University of Cape Town
Github Skills (43)
meteorology10
visualization10
evaluation10
survey10
forecast10
observation9
tsne9
earth-science8
grib8
atmosphere8
portal8
lifecycle7
netcdf7
apache-spark7
machine-learning7
Programming languages (2)
Github contributions (5)
MetOffice/data_science_cop
Apr 2020 - Sep 2022
A repository to put example and tutorial material created through the Met Office Data Science Community of Practice
Contributions:16 reviews, 53 commits, 20 PRs in 2 years 6 months
community-sciencepythonsciencedata-sciencemet
informatics-lab/precip_rediagnosis
Jan 2022 - Mar 2025
Project to use ML to re-diagnose precipitation fields from ensemble model fields
Contributions:15 reviews, 13 PRs, 84 pushes in 3 years 2 months
precipitationensemble-modelensemblemachine-learningdiagnose
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