Eric Cotner
Principal Data Scientist
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolEric Cotner is a Principal Data Scientist in San Diego with 11 years of experience applying mathematical optimization, simulation, experimentation, causal inference, and machine learning to supply chain and logistics problems. He leads production recommender and matching systems at Shipt, building optimization algorithms, APIs, and rigorous large-scale experiments to improve marketplace efficiency and worker satisfaction. Before transitioning to industry he earned a PhD in particle physics at UCLA, where he developed advanced numerical and analytical techniques studying cosmology and dark matter—an uncommon background that informs his quantitative rigor. He has driven end-to-end solutions across forecasting, routing, and inventory optimization at distributors and contributed practical time-series visualization examples to the widely used matplotlib project. Known for blending research-grade modeling with production engineering, he focuses on turning complex stochastic problems into robust, interpretable systems.
11 years of coding experience
9 years of employment as a software developer
University of California, San DiegoUniversity of California, Los Angeles
Spanish, English
Github Skills (11)
data-visualizations10
data-visualization10
data-visualisation10
histogram10
python10
plot10
data-science10
numpy10
matplotlib10
data-analysis9
time-series9
Programming languages (9)
Github contributions (5)
matplotlib/matplotlib
Oct 2020 - Oct 2020
matplotlib: plotting with Python
Role in this project:
Data Scientist
Data ScientistContributions:14 reviews, 8 commits, 1 PR in 12 days
Contributions summary:Eric primarily contributed to an example demonstrating time series visualization techniques within the matplotlib library. Their work involved generating synthetic time series data, experimenting with different visualization methods like `plt.plot` and 2d histograms (`plt.hist2d` and `plt.pcolormesh`), and refining the presentation with color scales, colorbars, and layout adjustments. The user also focused on optimizing the code and improving the clarity and readability of the example. The final result offers a more efficient and visually informative way to display and interpret time series data.
pythondata-sciencegtkdata-visualizationplotting
ecotner/solitons
Sep 2020 - Oct 2023
Numerical simulations of solitons
Contributions:2 PRs, 8 pushes, 3 branches in 3 years 1 month
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