Jason Keung
Senior Data Engineer
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Keung is a Senior Data Engineer with 11 years of experience building scalable data platforms and ML-focused pipelines at companies like Apple, Meta, and Capital One. He blends strong Python and SQL expertise with hands-on data warehousing, pipeline orchestration, and data modeling to turn business requirements into production systems. Trained in machine learning (Georgia Tech OMSCS), he partners with product and data science teams to operationalize models and has contributed to the popular Yellowbrick library, adding visualization examples and utilities that aid model selection. Jason is comfortable across the full SDLC, from requirements and architecture to troubleshooting and mentoring, and has a track record of introducing maintainable tooling (e.g., a custom Luigi solution) that enabled self-serve analytics. Based in New York, he pairs analytics rigor with pragmatic engineering to accelerate data-driven decisions.
11 years of coding experience
7 years of employment as a software developer
Master’s Degree, Machine Learning, Master’s Degree, Machine Learning at Georgia Institute of TechnologyBachelor's Degree, Integrated Business and Engineering (IBE) w/ focus in Industrial Engineering, GPA - 3.79, Bachelor's Degree, Integrated Business and Engineering (IBE) w/ focus in Industrial Engineering, GPA - 3.79 at Lehigh UniversityHigh School, High School at Old Bridge High School
Chinese, Spanish
Github Skills (9)
testing10
scikit-learn10
machine-learning10
visualization10
visualizations10
python10
scikit10
jupyter-notebook9
matplotlib8
Programming languages (4)
Github contributions (5)
DistrictDataLabs/yellowbrick
Jun 2016 - Aug 2017
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 8 PRs, 1 push in 1 year 2 months
Contributions summary:Jason contributed significantly to the development of the `yellowbrick` library, focusing on machine learning model analysis and visualization. The contributions involve the creation and modification of utility functions and tests to support the identification of model names. The user also added example notebooks, demonstrating the use of the library for visualizing learning curves and ROC curves, showcasing practical applications of the library's capabilities. These actions directly support the library's core function of helping users with machine learning model selection and analysis.
pythonvisual-analysisvisualizermodel-selectionmachine-learning
jkeung/yellowbrick
Jun 2016 - May 2018
Contributions:2 PRs, 31 pushes, 4 branches in 1 year 11 months
parameter-tuningvisual-analysismodel-selectionmachine-learningparameter
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