Cameron Pye
CEO at Unnatural Products
California, United States
Summary
👤
Senior🎓
Top SchoolCameron Pye is a chemist-turned-founder with 11 years of experience applying chemoinformatics and synthetic organic methods to drug discovery as CEO and co-founder of Unnatural Products, a startup using data science and parallel synthesis to unlock macrocycle therapeutics. He completed a PhD at UC Santa Cruz studying high-throughput screening, pharmacokinetics of bRo5 macrocycles, and cheminformatic analyses of natural product chemical space, pairing deep lab expertise with computational insight. Cameron’s hands-on lab skills—solid phase peptide synthesis and analytical chemistry—are balanced by practical software contributions to popular open-source tools like seaborn, where he improved code quality and robustness for statistical visualization. Based in California, he brings entrepreneurial leadership, rigorous experimental design, and an unusual blend of bench chemistry and data science fluency to tackle “undruggable” targets. Outside work he’s a runner, body surfer and climber, reflecting a disciplined, adventurous approach to problem solving.
11 years of coding experience
1 year of employment as a software developer
B.A. Chemistry, B.A. Chemistry at Whittier CollegeUniversity of California Santa Cruz
Stackoverflow
53reputation
4kreached
2answers
4questions
Github Skills (13)
data-visualizations10
pandas10
data-visualization10
data-visualisation10
python10
data-science10
matplotlib10
anaconda6
mingw6
sqlite6
sql6
mongodb6
mysql6
Programming languages (10)
Github contributions (5)
mwaskom/seaborn
May 2017 - Jul 2017
Statistical data visualization in Python
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 7 PRs, 6 comments in 1 month
Contributions summary:Cameron primarily contributed to the `seaborn` repository by modifying the `distributions.py` and `axisgrid.py` files. Their work involved addressing whitespace and pep8 style issues, adding a warning for a deprecated input method for `kdeplot`, and incorporating type checks for the `pairplot` function. These changes suggest a focus on improving code quality, addressing deprecation concerns, and enhancing the robustness of visualization functionalities within the library.
data-visualizationpythondata-sciencematplotlibpandas
pyeguy/MolNetwork
Aug 2016 - Sep 2018
Contributions:21 commits, 18 pushes, 2 branches in 2 years 1 month
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