Breaking Games Externship via ExternI combine statistical modeling and practical finance experience to analyze risk, build scalable data workflows, and support decision making.
Projects include K-means++ clustering on Apache Spark for retail scalability and multi-method analyses of UCI datasets, plus SQL and database architecture work in an externship with Breaking Games.
Built a K-means++ clustering model on Apache Spark to enhance scalability and efficiency for large retail datasets; highlighted limits in handling categorical and high-dimensional data and suggested hybrid and explainable AI solutions.
Applied data cleaning, visualization, statistical inference, hypothesis testing, k-means clustering, regression analysis, and cross-validation to UCI open datasets.
I combine statistical modeling and practical finance experience to analyze risk, build scalable data workflows, and support decision making.
I am Miao Yan, a graduate student at Duke University pursuing a Master of Engineering in Risk Engineering. I have internship experience in consulting, investment banking, and wealth management, and I work with Python, SQL, R, Power BI and statistical methods to turn data into disciplined risk and business insights.
Externships
SQL & Database Architecture Externship with Breaking Games
Breaking Games
Experience
Full-time Consulting Intern, Automotive Industry
Capgemini Invent · Sept. 2025 Dec. 2025
Full-time Investment Banking Intern
Haitong Securities · June 2024 Sept. 2024
Feiying Program Intern, Wealth Management Division
CITIC Securities, Wuchang Branch · Aug. 2023 Sept. 2023
Education
Duke University, Pratt School of Engineering
Master of Engineering, Risk Engineering
Wuhan University, School of Economics and Management
Bachelor of Economics in Finance | GPA: 3.7/4.0
Carnegie Mellon University, Data Science and Statistical Modeling with Python
Dual-Professor Research Program
Skills
Built a K-means++ clustering model on Apache Spark to enhance scalability and efficiency for large retail datasets; highlighted limits in handling categorical and high-dimensional data and suggested hybrid and explainable AI solutions.
Applied data cleaning, visualization, statistical inference, hypothesis testing, k-means clustering, regression analysis, and cross-validation to UCI open datasets.