Shuyi Lu - Senior Data Scientist

Shuyi Lu

Senior Data Scientist - AI Product

New York, New York, United States

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Summary

👤

Senior

🎓

Top School

Shuyi Lu is a Senior Data Scientist specializing in AI productization with seven years of experience building ML and NLP solutions for fintech and lending products. She holds an MA in Statistics from Columbia and blends strong statistical foundations with cloud-native deployment expertise to drive measurable business impact, such as $100K/month marketing savings and automated underwriting pipelines. Her work spans productionized NLP for unstructured transaction text (patented), OCR-driven document automation that cut review time ~80%, and graph-based fraud detection. Comfortable across the stack, she has also contributed full-stack improvements to the PEcAn open-source bioinformatics toolbox, improving UI/UX and robustness. Based in New York, she combines rigorous modeling, product-first engineering, and domain expertise in small business lending to translate complex data into operationalized, revenue-driving features.

7 years of coding experience

5 years of employment as a software developer

Exchange Program Economics, Exchange Program Economics at New York University

Master’s Degree Statistics, Master’s Degree Statistics at Columbia University

Bachelor’s Degree Mathematics, Bachelor’s Degree Mathematics at University of Iowa

English, Chinese

Github Skills (8)

shiny10

r10

data-science9

eco8

ecosystem8

ecos8

bayesian6

javascript4

Programming languages (2)

RJupyter Notebook

Github contributions (5)

PecanProject/pecan

Jun 2019 - Aug 2019

The Predictive Ecosystem Analyzer (PEcAn) is an integrated ecological bioinformatics toolbox.

Role in this project:

Full-stack Developer

Contributions:57 commits, 1 PR in 2 months

Contributions summary:Shuyi made several updates to the `pecanproject/pecan` repository, including adding progress bars to the benchmarking functionality and updating Shiny UI and server-side code. They also added try-catch statements to handle database connection errors and observe events in the Shiny application. These changes indicate a focus on improving the user interface and functionality of the web application, especially related to the benchmarking module. They also worked on the UI for the plots and data plots, and added options to them.

forecastingplantspecanbioinformaticscyberinfrastructure

sl4397/GSoc_Vis

Mar 2019 - Apr 2019

Contributions:10 pushes, 1 branch in 12 days

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