Sam Levang
Chief Scientist at Salient Predictions
Greater Boston United States
Summary
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Rockstar🎓
Top SchoolSam Levang is a Chief Scientist based in Greater Boston with seven years of experience translating physical oceanography research into data-driven products and algorithms. With a PhD-trained background from MIT and Woods Hole, Sam blends rigorous academic expertise in ocean dynamics with hands-on leadership at Salient Predictions, steering applied science efforts since 2019. He brings practical engineering chops too—contributing backend fixes and new functionality to the widely used xarray Python library, including a dataset-level curvefit integration with scipy.optimize and nuanced bug fixes that show deep familiarity with array-backed data models and Zarr storage. Comfortable moving between research, production code, and documentation, Sam is skilled at turning complex scientific problems into reliable, reproducible software. Colleagues value his ability to bridge domain science and engineering, and his early seafaring and lab roles hint at a pragmatic, field-tested approach to problem solving.
7 years of coding experience
4 years of employment as a software developer
PhD, Physical Oceanography, PhD, Physical Oceanography at Woods Hole Oceanographic InstitutionPhD, Physical Oceanography, PhD, Physical Oceanography at Massachusetts Institute of TechnologySea Education Association | SEA SemesterBA, Physics, BA, Physics at Macalester College
Github Skills (8)
pandas10
xray10
python10
numpy10
xa10
scipy9
dask8
netcdf7
Programming languages (2)
Github contributions (5)
pydata/xarray
Mar 2021 - Dec 2022
N-D labeled arrays and datasets in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:41 reviews, 6 commits, 17 PRs in 1 year 8 months
Contributions summary:Sam contributed to the xarray library by implementing features and fixing bugs related to core functionality. Their work included adding a `curvefit` method for dataset objects, which integrates with `scipy.optimize`. They also addressed issues related to attribute handling in the `xr.where` function and improved the handling of non-existent Zarr stores and dtype consistency in rolling mean calculations, demonstrating a deep understanding of the library's internal workings. They also contributed to the documentation, specifically, updating the 'whats-new' sections.
pythonnetcdfnumpydaskarrays
slevang/xarray
Jan 2021 - Mar 2025
N-D labeled arrays and datasets in Python
Contributions:77 pushes, 16 branches in 4 years 1 month
datasetspythonarrays
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