Ruize Jin

I’m Ruize Jin, and you can call me Jerry. I’m a Master of Quantitative Management: Business Analytics student at Duke University, with an undergraduate background in actuarial science from the Central University of Finance and Economics.

I started in actuarial science and spent much of my undergraduate years building a foundation in probability, statistics, risk modeling after taking tons of professional actuarial designations. Over time, however, I realized that the most valuable thing actuarial training gave me was not simply knowledge of insurance. It was a way of thinking: using data to understand uncertainty, build predictive models, and make better decisions when outcomes are never fully certain.

That mindset gradually pulled me toward data science. During college, I took courses in machine learning, deep learning, and NLP across different departments, while applying those skills through data-focused internships and projects. The more I worked with real data, the more interested I became in a broader question: how can we turn data and AI into tools that actually improve the way people make decisions and work?

That question now shapes the projects I choose to build. Rather than treating data science as an exercise in applying another model to another benchmark dataset, I want to work on problems that have a clear reason to exist beyond the model itself. I’m especially interested in applied machine learning and AI systems that connect technical modeling with real-world use — whether that means making black-box models more interpretable, turning a prediction model into a usable product, or building AI workflows that automate repetitive work while keeping people in the loop.