Yanbing Dong
产品经理 at 美团
Beijing, China
Summary
👤
Senior🎓
Top SchoolYanbing Dong is a product manager and aspiring financial analyst with 10 years of cross-industry experience spanning fintech, e-commerce, consulting, and audit, holding a Master’s in Enterprise Risk Management from Columbia and dual degrees in Finance and Arabic. He combines strong financial analysis and risk assessment skills with hands-on data tooling (SQL, Python, Excel) to build dashboards, streamline workflows, and improve decision-making—previous projects delivered up to 25% faster insights and 30% improved onboarding efficiency. At Meituan he translated complex operational metrics into actionable product improvements across Hong Kong and Saudi markets, and at PwC and Kearney he produced audit-grade risk reports and contract-led negotiation support. Unusually for a product professional, he also contributes to open-source ML projects—optimizing memory usage and bug fixes in a well-known Imagen PyTorch implementation—demonstrating a practical blend of quantitative, technical, and multicultural collaboration. Yanbing is seeking roles where financial analytics, risk management, and product operations intersect to drive measurable business impact.
10 years of coding experience
Master's degree, Enterprise Risk Management, Master's degree, Enterprise Risk Management at Columbia UniversityBachelor's degree, Finance, Bachelor's degree, Finance at Beijing Foreign Studies University
English, Chinese, Arabic
Github Skills (7)
pytorch10
machine-learning10
artificial-intelligence10
deep-learning10
python10
model-optimization9
text-to-image9
Programming languages (10)
Github contributions (5)
lucidrains/imagen-pytorch
Jul 2022 - Aug 2022
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 7 PRs, 1 comment in 27 days
Contributions summary:Yanbing primarily contributed to the Imagen PyTorch repository by fixing bugs and optimizing the existing code. Their work included correcting issues in the `cast_tuple` and `optimizer_key` functions within the trainer, as well as addressing a typo in `self_cond`. Furthermore, the user implemented optimizations, such as loading models to CPU to reduce GPU memory usage, and also corrected the calculation of the true learning rate from optimizers that implement warmup schedulers.
neural-networkpytorchtext-to-imageartificial-intelligencedeep-learning
animebing/mnist_tsne
Mar 2018 - Mar 2018
t-sne visualization of mnist images when feature is represented by raw pixels and cnn learned feature
Contributions:30 commits, 26 pushes, 1 branch in 4 days
mnisttsnevisualizationt-sne
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