Charlie Brummitt
Principal Data Scientist
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolCharlie Brummitt is a Principal Data Scientist with 14 years of experience melding applied mathematics, complex-systems research, and machine learning to tackle real-world problems across economics, agriculture, and climate. He leads data science teams at Indigo, where he designs rigorous MRV systems and co-authors methodologies that quantify carbon credits at unprecedented scale and statistical rigor. His background spans academic postdocs at Columbia and Harvard and a Ph.D. in applied math, informing work on contagion in supply chains, financial crises, and the social dynamics of innovation. Charlie blends statistical theory with practical engineering—contributing to open-source tools like pyGAM to add posterior sampling and uncertainty quantification—and has built chatbots and NLP pipelines for randomized trials in developing economies. He thrives in ambiguity and excels at synthesizing ideas from disparate fields to create auditable, policy-relevant scientific products.
14 years of coding experience
5 years of employment as a software developer
Ph.D Applied Mathematics, Ph.D Applied Mathematics at University of California, DavisB.S Mathematics Physics, B.S Mathematics Physics at University of Wisconsin-Madison
German, English, Spanish
Stackoverflow
651reputation
182kreached
5answers
1question
Github Skills (13)
machine-learning10
python10
data-science10
numpy10
distributions9
scipy9
probability-distribution9
keras-layer6
pandas6
dictionary6
tensorflow6
convex-hull6
jupyter6
Programming languages (6)
Github contributions (5)
dswah/pyGAM
Jul 2017 - Dec 2017
[HELP REQUESTED] Generalized Additive Models in Python
Role in this project:
Data Scientist
Data ScientistContributions:16 commits, 2 PRs, 26 comments in 4 months
Contributions summary:Charlie primarily focused on implementing and refining methods for simulating from the posterior distribution of model coefficients and the response variable within the pygam library. Their contributions involved adding the `sample` method to the `Distribution` and `GAM` classes, and improving the parametrizations of the distributions. These changes provide users with the ability to draw samples from the model's posterior, enabling uncertainty quantification and facilitating more thorough model evaluation.
pythondata-scienceadditive-modelsscientific-computingmachine-learning
cbrummitt/machine_learned_patterns_in_economic_development
Oct 2018 - Nov 2019
Paper on identifying patterns in economic development using statistical learning
Contributions:14 commits, 2 PRs, 9 pushes in 1 year 1 month
pythoneconomic-developmentpatternspattern-recognitiongeneralized-additive-models
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