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I am a fourth-year Actuarial Science & Statistics student at the University of Toronto, pursuing the SOA path. I thrive in dynamic, high-pressure environments and enjoy turning data into insights that inform risk and decision-making.\n\nThrough coursework, case competitions, and extracurricular activities, I have built strong analytical and problem-solving skills and I am eager to apply my actuarial training to the insurance industry and take on new challenges.…I am a fourth-year Actuarial Science & Statistics student at the University of Toronto, pursuing the SOA path. I thrive in dynamic, high-pressure environments and enjoy turning data into insights that inform risk and decision-making.\n\nThrough coursework, case competitions, and extracurricular activities, I have built strong analytical and problem-solving skills and I am eager to apply my actuarial training to the insurance industry and take on new challenges.

Jun Chen Li

Data Scientist, Programmer, Web Developer, +1

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I am a fourth-year Actuarial Science & Statistics student at the University of Toronto, pursuing the SOA path. I thrive in dynamic, high-pressure environments and enjoy turning data into insights that inform risk and decision-making.\n\nThrough coursework, case competitions, and extracurricular activities, I have built strong analytical and problem-solving skills and I am eager to apply my actuarial training to the insurance industry and take on new challenges.…I am a fourth-year Actuarial Science & Statistics student at the University of Toronto, pursuing the SOA path. I thrive in dynamic, high-pressure environments and enjoy turning data into insights that inform risk and decision-making.\n\nThrough coursework, case competitions, and extracurricular activities, I have built strong analytical and problem-solving skills and I am eager to apply my actuarial training to the insurance industry and take on new challenges.

Available to hire

I am a fourth-year Actuarial Science & Statistics student at the University of Toronto, pursuing the SOA path. I thrive in dynamic, high-pressure environments and enjoy turning data into insights that inform risk and decision-making.\n\nThrough coursework, case competitions, and extracurricular activities, I have built strong analytical and problem-solving skills and I am eager to apply my actuarial training to the insurance industry and take on new challenges.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Team Lead at University of Toronto

December 1, 2024 - December 1, 2024

Loss Development Analysis of Accident Benefits: Medical Rehabilitation — Led a 5-member team in a comprehensive analysis of accident benefits loss development using historical medical claims and financial data to identify cost drivers; applied advanced actuarial techniques such as Expected Loss method and BF method to reduce medical rehabilitation claims during Covid-19; communicated variability of ultimate claims through clear visualization and Excel reporting.

Research Analyst at University of Toronto

December 1, 2024 - December 1, 2024

Data Validation Analysis: Factors Affecting Happiness across Countries — Conducted linear regression analysis to assess the impact of GDP per capita and life expectancy on happiness scores, supported by validation techniques such as AIC model selection, variance of inflation factors, and handling extreme observations; applied Box-Cox transformation to correct data violations and ensure model assumptions; validated model performance by detecting overfitting and ensuring adherence to key regression assumptions.

Project Lead at University of Toronto (Wawanesa Case Competition: Optimizing Homeowners Insurance Loss Control)

December 1, 2024 - December 1, 2024

Developed a data-driven insurance risk management strategy to provide competitive premiums for home insurance and improve homeowner safety; scraped and consolidated historical loss data related to fire, hail, and wind from news sources; built a predictive model in R & Python achieving 87% accuracy in estimated disaster damage; delivered insights through descriptive analytics, benchmarking, and AI-based risk prediction.