Yijing Chen
Engineering Manager, Machine Learning at Meta
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYijing Chen is an Engineering Manager in Machine Learning at Meta with a decade of experience building and scaling ML solutions across industry leaders including Meta and Microsoft. She combines hands-on expertise in time series forecasting, cloud AI services, and production video ML with a strong foundation in statistics from Harvard and applied analytics from the University of Michigan. At Microsoft she delivered end-to-end AI solutions for enterprise customers using Hadoop/Spark and Azure ML, and at Meta she progressed from Senior ML Engineer to Staff and now Manager, driving video ML and cross-functional delivery. Her open-source contributions to practical tutorials on deep learning for time series reflect a pragmatic focus on data preparation and reproducible implementations. Colleagues describe her as a leader who bridges engineering, product, and data science to turn complex models into reliable production systems.
10 years of coding experience
10 years of employment as a software developer
Bachelor of Arts (BA) Economics & Applied Statistics, Bachelor of Arts (BA) Economics & Applied Statistics at University of MichiganMaster's degree Statistics, Master's degree Statistics at Harvard University
English, Chinese, Chinese
Github Skills (11)
data-preprocessing10
jupyter-notebook10
machine-learning10
time-series-forecasting10
dataprep10
deep-learning10
python10
image-processing7
data-visualizations7
data-visualisation7
data-visualization7
Programming languages (2)
Github contributions (5)
Azure/DeepLearningForTimeSeriesForecasting
Sep 2018 - Aug 2019
A tutorial demonstrating how to implement deep learning models for time series forecasting
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 24 commits, 1 PR in 10 months
Contributions summary:Yijing primarily contributed to the data setup and data preparation within the context of time series forecasting using deep learning models. Their contributions included updating data setup code, modifying image plot functions, and merging branches. The user's work involved preparing the data for use in machine learning models, with changes in the notebooks and documentation indicating an emphasis on practical implementation within the project's goals.
forecastingpytorchdeep-learningseries-forecastingmachine-learning
Azure/cortana-intelligence-energy-demand-forecasting
Oct 2016 - Sep 2018
Contributions:84 commits, 21 PRs, 31 pushes in 1 year 11 months
energy-industryend-to-endindustrycortanaenergy
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