Denny Chen is a Senior Data Scientist with six years of experience applying machine learning, statistical modeling, and AI to finance and risk analytics. He has led production credit scorecard development and built scalable ETL pipelines ingesting 130M+ records at PwC, and optimized LLM pipelines for financial document tracking at Fidelity. His toolkit spans Python, R, SQL, PyTorch, Spark and cloud deployment, and he complements technical depth with Bayesian and time-series expertise from academic consulting at Boston University. Known for pragmatic engineering—e.g., inventing a monotone optimal binning algorithm that accelerated projects—he blends model rigor with delivery focus to turn complex regulatory and risk problems into actionable systems. Based in Atlanta, he is open to collaborations in quantitative research, risk modeling, and applied NLP for financial use cases.
6 years of coding experience
3 years of employment as a software developer
Bachelor of Business Administration - BBA, Finance, General, Bachelor of Business Administration - BBA, Finance, General at National Central University
Master of Science - MS, Statistics, 3.93, Master of Science - MS, Statistics, 3.93 at Boston University Graduate School of Arts & Sciences
This project mainly implements the Monotonic Optimal Binning(MOB) algorithm in SAS 9.4. We extend the application of this algorithm which can be applied to numerical and categorical data. In order to avoid the problem of creating too many bins, we optimize the p-value iteratively and provide bins size first binning, monotonicity first binning, and chi merge binning methods for users to discretize data more conveniently.
Contributions:1 release, 40 commits, 2 PRs in 3 months
Reproducible data science with R, RStudio, Git, and GitHub
Contributions:1 push in 4 years 3 months
sciencedata-sciencecmlrstudiorstats
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