Blake Atkinson
Data Scientist
Fort Thomas, Kentucky, United States
Summary
👤
Senior🎓
Top SchoolBlake Atkinson is a data scientist with nine years of experience who blends applied math and engineering to build production-ready models, particularly in sports analytics. He has led full-stack data science work at G Street Analytics—creating soccer team and player models, automating complex pipelines, and applying time-series, tabular, and graph models—and now applies that expertise at Angstrom. Blake has a strong probabilistic modeling background demonstrated by contributions to the well-regarded NGBoost project, where he implemented Poisson support and example workflows including SHAP analysis. Trained at the University of Michigan's School of Information and with a physics BA, he pairs rigorous analytical instincts with practical software skills from freelance systems and web development. Open to staying in sports or moving into other domains, he is actively expanding into graph neural networks to broaden his impact.
9 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Applied Data Science, Master of Science - MS, Applied Data Science at University of Michigan - School of InformationBachelor of Arts - BA, Physics, Bachelor of Arts - BA, Physics at Transylvania University
Github Skills (15)
probabilistic-models10
data-modeling10
scikit10
machine-learning10
probabilistic-programming10
statistical-models10
python10
numpy10
probabilistic-reasoning10
scikit-learn10
gradient-boosting9
pandas9
matplotlib8
shap8
seaborn7
Programming languages (3)
Github contributions (5)
stanfordmlgroup/ngboost
Jul 2020 - Aug 2020
Natural Gradient Boosting for Probabilistic Prediction
Role in this project:
Data Scientist / ML Engineer
Data Scientist / ML EngineerContributions:9 commits, 1 PR, 3 comments in 1 month
Contributions summary:Blake primarily contributed to implementing the Poisson distribution within the NGBoost framework. Their work involved adding the `Poisson` class, implementing `PoissonLogScore`, and defining the `fit` and `sample` methods. They also created an example demonstrating the use of the Poisson distribution within NGBoost, including feature importance analysis and SHAP plots. The user's contributions demonstrate a strong understanding of probabilistic modeling and machine learning concepts.
pythonpredictionnaive-bayesnatural-gradientsmachine-learning
btatkinson/golf_scraper
Apr 2019 - Jun 2019
Contributions:15 commits, 14 pushes, 1 branch in 2 months
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