Mason Gallo
Principal Data Scientist Machine Learning Engineer
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolMason Gallo is a Principal Data Scientist and Machine Learning Engineer based in New York with 12 years of experience building production ML systems and tooling across startups and tech giants. He has held machine learning and software engineering roles at Season, Meta, Quartet Health, Google (Summer of Code), and major agencies, blending product-focused modeling with scalable engineering. Mason contributes to open-source machine learning tooling—improving hyperparameter tuning and partial dependence visualizations in the widely used mlr R package—reflecting a focus on interpretable model insights. He holds an M.S. in Computer Science from Georgia Tech and combines academic rigor with hands-on deployment experience, often surfacing subtle data-visualization and validation improvements that make models easier to trust in production.
11 years of coding experience
10 years of employment as a software developer
Master of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at Georgia Institute of TechnologyBS, BS at Washington University in St. Louis
Github Skills (11)
hyperparameter-optimization10
machine-learning10
data-science10
r10
ggplot9
fine-tuning9
predictive-modeling9
mlr9
performance-tuning9
auto-tuning9
r-package8
Programming languages (3)
Github contributions (5)
mlr-org/mlr
Mar 2016 - May 2019
Machine Learning in R
Role in this project:
Data Scientist
Data ScientistContributions:21 commits, 25 PRs, 70 pushes in 3 years 3 months
Contributions summary:Mason's commits primarily involve modifications to the `mlr-org/mlr` repository, focusing on enhancements related to hyperparameter tuning and partial dependence calculations. They contributed by adding support for visualizing tasks with two or more hyperparameters, and improved hyperparameter effect visualizations through changes that add a nested cross validation facet for improved analysis. Furthermore, the user fixed bugs and added support for integer features within the grid for partial dependence, improving the robustness of the visualization capabilities.
imbalance-correctionlearnersensemble-learningclassificationr-package
MasonGallo/pandas
Feb 2016 - Mar 2018
Contributions:22 pushes, 7 branches in 2 years 1 month
manipulationmanipulation-librarydata-analysispythondata
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