Mingjie Wang
Senior Researcher at 未公开的对冲基金
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolMingjie Wang is a Senior Researcher based in Beijing with 10 years of experience building factor research and deep learning models for medium- and low-frequency quantitative strategies at hedge funds. A Kaggle Competition Master and winner of the Jane Street Market Prediction challenge, he routinely applies top-tier machine learning skills—from tabular regression to NLP and computer vision—to real-world trading and research problems. His background spans academic research (MPhil) and industry internships in AI and NLP, with published work and competitive placements in SIGSPATIAL and multiple Kaggle contests. Mingjie is skilled at turning academic-grade models into production-ready trading signals and is comfortable across the full ML stack, including attention/BERT-style architectures and market-focused factor construction. He combines a quantitative trading focus with an uncommon habit of sharpening his models through high-stakes public competitions, giving him both rigor and creative problem-solving edge.
10 years of coding experience
1 year of employment as a software developer
Exchange Programme, Computer Science and Technology, Exchange Programme, Computer Science and Technology at 兰州大学Master of Philosophy - MPhil, Computer Science and Technology, Master of Philosophy - MPhil, Computer Science and Technology at 北京师范大学-香港浸会大学联合国际学院Master of Philosophy - MPhil, Computer Science and Technology, Master of Philosophy - MPhil, Computer Science and Technology at 香港浸会大学
Github Skills (11)
finance10
pandas10
financial10
stock-data10
fintech10
python10
financial-data10
server1
docker1
nodejs1
server-side1
Programming languages (2)
Github contributions (5)
JoinQuant/jqdatasdk
Oct 2017 - Jun 2018
简单易用的量化金融数据包(easy utility for getting financial market data of China)
Contributions:61 commits, 1 PR, 1 push in 8 months
financialfinancechinamarket-datamarket
buaawcy/OC_Code
Feb 2016 - Apr 2016
Contributions:42 pushes, 1 branch in 1 month
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