Jie Zhang
商业化产品运营 at 字节跳动
Yangpu District, Shanghai, China
Summary
👤
Senior🎓
Top SchoolJie Zhang is a product operations professional with 12 years of experience, currently driving commercialization and bidding product operations at ByteDance in Shanghai. She has a strong track record running large promotional campaigns and marketplace growth initiatives across platforms like Shopee and Ele.me, blending data-driven campaign strategy with merchant growth tactics. Unusually for a product operator, Jie contributes to open-source deep learning projects (MXNet, Caffe derivatives) as a backend/ML engineer, demonstrating hands-on familiarity with model runtimes, debugging, and performance tuning. Her background in tourism management from Fudan University informs a user-centric lens on product and activity design, while her cross-border e-commerce experience equips her to scale merchant programs across diverse markets.
12 years of coding experience
学士, 旅游管理, 学士, 旅游管理 at 复旦大学
Github Skills (17)
debug10
caffe10
c-language10
python10
mxnet10
cmake10
mask-rcnn10
deep-learning10
performance-optimization10
faster-rcnn10
computer-vision10
cprogramming-language10
machine-learning9
openblas9
data-augmentation9
Programming languages (8)
Github contributions (5)
luoyetx/deep-landmark
Jun 2015 - Mar 2020
Predict facial landmarks with Deep CNNs powered by Caffe.
Role in this project:
ML Engineer
ML EngineerContributions:46 commits, 1 PR, 7 pushes in 4 years 9 months
Contributions summary:Jie focused on developing and testing a facial landmark prediction model using a deep CNN architecture, as evidenced by the commit messages and code changes. Their contributions include fixing errors, removing and adding features such as data augmentation, and implementing evaluation metrics to assess performance. They also implemented tests for different model components and made modifications to the data preparation pipeline. The user's work centered around refining the model architecture and improving its accuracy, as demonstrated by changes to CNN layers, and model parameters.
pythondeep-learningcnnspredictcaffe
apache/mxnet
Dec 2016 - Jan 2018
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 6 PRs, 16 comments in 1 year
Contributions summary:Jie's commits primarily focus on improving the functionality and stability of the MXNet framework. They implemented support for monitoring auxiliary parameters, added logging for saving model parameters and optimizer states, and addressed several bugs related to image encoding and metric updates. Their work also included fixing timestamp issues in the profiler and ensuring correct initialization of the KVStore before loading states. These contributions demonstrate a focus on debugging, enhancing performance, and ensuring proper functionality.
pythonschedulerdataflowmutationdata-science
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