Paul Hobson
Senior Environmental Data Engineer
Portland, Oregon, United States
Summary
🤩
Rockstar🎓
Top SchoolPaul Hobson is a Senior Environmental Data Engineer and licensed professional engineer in Oregon with 16 years of experience applying data management, machine learning, and scientific computing to water resources, numerical modeling, and environmental remediation. He blends deep academic training in environmental fluid mechanics and sediment transport from Georgia Tech with hands-on expertise in the scientific Python stack, SQL, and web APIs (C# and Python) to deliver practical, production-ready engineering solutions. Paul has advanced hydrologic and hydraulic modeling through distributed computing and optimization at firms from Geosyntec to Confluency, and now leads data engineering work at Herrera Environmental Consultants. An active open-source contributor, he has improved reliability and visualization quality in high-profile projects like matplotlib, seaborn, and statsmodels—work that underscores his attention to code quality and reproducible science. Outside of work he directs that same rigor toward outdoor advocacy and maintaining community scientific tools.
15 years of coding experience
19 years of employment as a software developer
Master's degree Civil Engineering, Master's degree Civil Engineering at Georgia Institute of Technology
Stackoverflow
66,812reputation
10.9mreached
580answers
16questions
pandas
top-1%
matplotlib
top-1%
boxplot
top-5%
image
top-1%
pyodbc
top-5%
fft
top-1%
Github Skills (52)
data-analysis10
data-visualizations10
debug10
scipy10
seaborn10
python10
data-science10
image10
matplotlib10
spyder10
pandas10
statistics10
ipython10
data-visualisation10
hypothesis-testing10
Programming languages (12)
Github contributions (5)
matplotlib/matplotlib
Mar 2013 - Jan 2020
matplotlib: plotting with Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 261 commits, 150 PRs in 6 years 10 months
Contributions summary:Paul primarily contributed to bug fixes and code improvements related to the boxplot functionality within the matplotlib library. Their work involved modifying the `_axes.py` file to address issues with marker attributes and extra blank lines. Additionally, the user removed extra plugin calls and added documentation for the `mpl-proscale` package. The commits reflect a focus on improving the reliability, code quality, and documentation of the plotting library.
pythondata-sciencegtkdata-visualizationplotting
statsmodels/statsmodels
Jul 2012 - Jun 2016
Statsmodels: statistical modeling and econometrics in Python
Role in this project:Data Scientist
Data ScientistContributions:43 commits, 2 PRs, 39 comments in 3 years 11 months
Contributions summary:Paul primarily contributed to the statistical modeling and econometrics aspects of the `statsmodels` repository. Their commits focused on enhancing the functionality of QQ plots, adding the capability to display theoretical probabilities, and including test coverage for the new features. Further work included refining documentation and addressing code style issues to adhere to PEP8 standards. The user also made the code more robust and included an example of plotting two-sample QQ plots.
forecastingpythonregression-modelsstatsmodelsstatistics
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