Shay Chen
Machine Learning Intern @Legal Chain
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WORK HISTORY
Machine Learning Intern @Legal Chain
US
EDUCATION
Guangdong University of Foreign Studies
Bachelor of Science - BS, Accounting
University of Southern California
Master of Science - MS, Financial Engineering
ABOUT SHAY CHEN
I’m a results-driven Quantitative Analyst and M.S. Financial Engineering candidate at the University of Southern California, passionate about applying quantitative models, financial theory, and data science to strengthen market risk management and investment decision-making.My experience spans both financial modeling and technical implementation. At J.P. Morgan’s virtual program, I built commodity pricing models and credit risk simulations using Python to forecast natural gas price movements and segment client credit exposure. At Legal Chain, I engineered AI-enabled backend systems using Prisma, Supabase, and OpenAI APIs, integrating NLP and data pipelines that automated document analysis and reporting—translating complex machine-learning solutions into operational impact.Previously at KPMG’s Risk Consulting and Grant Thornton Audit, I developed dashboards, internal-control reports, and risk assessments that strengthened governance frameworks and compliance visibility.With strong proficiency in Python, R, SQL, and quantitative methods—including VaR/CVaR, Monte Carlo simulation, stress testing, and factor modeling—I aim to bridge the gap between quantitative rigor and business strategy.I’m driven to contribute to teams focused on market risk oversight, model validation, and portfolio analytics, where analytical precision and data-driven insights directly support risk-adjusted performance and capital efficiency.Looking forward to connect here!
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