Luyao Lin
About Luyao Lin
Luyao Lin is a Senior Data Scientist with extensive experience in quantitative modeling and risk management. He has worked at various financial institutions and holds a PhD in Statistics from Simon Fraser University.
Work at Intact Financial Corporation
Luyao Lin currently serves as a Senior Data Scientist at Intact Financial Corporation, a position held since 2022. Prior to this role, Lin worked as a Data Scientist II at Intact from 2020 to 2022. In these roles, Lin has contributed to various data-driven projects within the organization, focusing on risk management and quantitative analysis.
Previous Experience in Data Science and Risk Analysis
Before joining Intact, Luyao Lin worked at the Insurance Corporation of British Columbia (ICBC) as a Data Scientist from 2019 to 2020. Lin also held the position of Model and Quantitative Specialist at Shanghai Pudong Development Bank from 2012 to 2013. Earlier in their career, Lin served as a Risk Analyst at AIA from 2008 to 2011, gaining extensive experience in quantitative modeling for risk management across banking and insurance sectors.
Education and Expertise
Luyao Lin holds a Doctor of Philosophy (PhD) in Statistics from Simon Fraser University, where research focused on the design and analysis of computer experiments and statistical analysis for large data sets. Lin also earned a Master of Science (MSc) in Actuarial Science from Simon Fraser University and a Bachelor of Science (BSc) in Financial Mathematics from Peking University. Lin has expertise in Bayesian inference and stochastic modeling, complemented by completing the first five Society of Actuaries (SOA) exams and CFA Level I and II certifications.
Teaching and Research Experience
Luyao Lin has academic experience as a Teaching Assistant and Research Assistant at Simon Fraser University from 2006 to 2007. This role involved supporting faculty in delivering course content and assisting in research projects, contributing to Lin's strong foundation in statistical analysis and quantitative methods.