Ryan Grout is a seasoned Python engineer with over a decade of hands-on experience developing and maintaining core tooling for scientific and data ecosystems, including significant contributions to Anaconda, conda, and conda-build. His work spans back-end systems, build and release automation, and performance tuning—evidenced by contributions to high-profile open-source projects like JupyterLab, Numba, and Scalene. A math graduate with a CS minor from Brigham Young University, he helped design an immersive applied-computational mathematics curriculum and authored much of its lab material used across six semesters. At Continuum Analytics and in open-source, he solved cross-platform packaging and compilation issues across Linux, macOS, and Windows, and helped port Anaconda to ARM (Raspberry Pi). He pairs rigorous testing and validator improvements with practical engineering (e.g., adding nextafter to Numba and improving CSV viewing/search in JupyterLab), and has experience mentoring teams in git workflows and reproducible builds. Based in Austin, he balances technical problem solving with a creative eye—his hobbyist photography reflects an appreciation for elegance that shows up in clean, performance-minded code.
11 years of coding experience
9 years of employment as a software developer
Missouri State University
Bachelor of Science (BS), Mathematics, Bachelor of Science (BS), Mathematics at Brigham Young University
Contributions:2 reviews, 53 commits, 22 PRs in 3 years 4 months
Contributions summary:Ryan primarily focused on maintaining and updating the `toolz` library, a functional library for Python. Their contributions involved removing Python 2 compatibility code and other Python 2/3 specific code. They also refactored parts of the library to remove unnecessary imports and updated testing infrastructure.
Contributions:82 commits, 31 PRs, 17 pushes in 8 months
Contributions summary:Ryan primarily contributed to the build process and recipes for the conda package manager. Their commits focused on modifying build scripts (`build.sh`, `bld.bat`) and related configuration files, including the addition of dependencies, fixes for existing recipes, and adjustments for different operating systems (Darwin, Linux, Windows). They also introduced and updated scripts for activating and deactivating environments and implemented a runtime test.
recipespythoncondasetuptoolsanaconda
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