Jun Zhang
数据智能研发部副总裁 at CreditEase
Chaoyang District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolJun Zhang is a data intelligence leader with 11 years of experience building ML-driven products and teams, currently serving as VP of Data Intelligence R&D at CreditEase in Beijing. He progressed from hands-on research roles at Fujitsu and Baidu to senior engineering and innovation leadership, blending deep technical know-how with strategic product delivery. His work includes practical contributions to the PaddlePaddle community—implementing CNN-based image classification and image search notebooks—showing continuous engagement with open-source deep learning. Trained at Harbin Institute of Technology (BS/MS in Computer Science), he is comfortable moving between research prototypes and production-grade systems. Colleagues know him for translating abstract ideas into concrete models and pipelines, a trait hinted at by his childhood metaphor of treasure maps guiding his thinking. He balances technical depth and organizational leadership to drive data products that materially impact business outcomes.
11 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Harbin Institute of Technology
Japanese, English, Chinese
Github Skills (7)
neural-network10
paddlepaddle10
computer-vision10
machine-learning10
convolutional-neural-networks10
python10
image-classification9
Programming languages (12)
Github contributions (5)
PaddlePaddle/book
Aug 2020 - May 2021
Deep Learning 101 with PaddlePaddle （『飞桨』深度学习框架入门教程）
Role in this project:
ML Engineer
ML EngineerContributions:30 reviews, 16 commits, 44 PRs in 9 months
Contributions summary:Jun's contributions center around implementing and improving machine learning models within the `paddlepaddle/book` repository, which focuses on deep learning with PaddlePaddle. The commits demonstrate a clear focus on image-related tasks, evidenced by the "image search notebook" and "cnn based image classification" updates. Their work involves normalizing data, defining model architectures (specifically Convolutional Neural Networks), and setting up training and validation loops.
deep-learningpytorchpaddlepaddletensorflow
jzhang533/Paddle
Jul 2020 - Apr 2025
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Contributions:10 PRs, 85 pushes, 27 branches in 4 years 9 months
pytorchparalleldeep-learningreinforcement-learningindustrial
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