Yutong Chen is a data engineer and statistician with a decade of experience turning complex financial data into operational efficiency and predictive insight across major banks including Truist, Citi, and Pinnacle Financial Partners. She combines strong SQL-based ETL and pipeline engineering with Python-driven statistical modeling and machine learning to automate merger tracking, forecast loan performance, and predict employee attrition, cutting manual work and reducing risk. Based in Atlanta, she also contributes to open-source ML work—implementing and refining a WGAN-div model in PyTorch—demonstrating hands-on expertise in deep learning beyond typical analytics roles. A collaborator with clear communication and a track record in Tableau visualization, she bridges technical rigor from her MS in Statistics with practical business impact in regulated finance.
9 years of coding experience
4 years of employment as a software developer
Bachelor's degree Finance, Bachelor's degree Finance at China Agricultural University
Master of Science - MS Statistics, Master of Science - MS Statistics at Georgia State University
Middle & High School Diploma, Middle & High School Diploma at Beijing National Day School
PyTorch implementations of Generative Adversarial Networks.
Role in this project:
ML Engineer
Contributions:9 commits, 1 PR, 2 comments in 9 days
Contributions summary:Yutong implemented and refined a Wasserstein GAN with a divergence penalty (WGAN-div) model in PyTorch within this repository. Their contributions included defining the model architecture, loss functions, and training loops. Subsequent commits addressed formatting issues and corrected bugs to ensure proper GPU execution.
Contributions:23 commits, 20 pushes, 1 branch in 9 months
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Yutong Chen - Data Analytics And Operations Officer