Justin Lee is a Senior Product Analyst and data scientist with a decade of experience applying statistical modeling, machine learning, and NLP across fintech, e-commerce, and consulting. Currently at Coupang, he drives product analytics for delivery and merchant experiences while serving on hiring and data logging committees, blending hands-on modeling with product-facing decision support. His background includes building wavelet variance R packages and interactive R Shiny tools during academic research, reflecting a strong foundation in time series, signal processing, and reproducible open-source work. Previously he delivered AI and forecasting solutions at Deloitte and NH Investment & Securities, and designed CRM analytics for restaurant clients that translated into operational savings. Comfortable moving between research-grade code (Rcpp) and production analytics, he brings both academic rigor and pragmatic product impact to solve real-world problems in Seoul.
9 years of coding experience
7 years of employment as a software developer
Master of Science - MS, Statistics, 3.83, Master of Science - MS, Statistics, 3.83 at Penn State University
:alarm_clock: This R package provides the tools to perform standard and robust wavelet variance analysis for time series (signal processing). Among others, aside from computing the wavelet variance and cross-covariance (classic and robust), the package provides inference tools (e.g. confidence intervals) and plotting tools allowing to perform some visual analysis and assess the properties of the underlying time series.
Contributions:226 commits, 152 pushes, 2 comments in 6 months
Contributions:140 commits, 70 pushes, 4 branches in 6 months
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