Amazon Externship via ExternMaster's student preparing for a first professional role, focused on learning and hands-on projects.
A small portfolio of academic and personal projects completed while pursuing my master's, showing skills I am developing and the types of problems I tackle.
Analyzed 204,000+ property records and engineered predictive features from housing descriptions, building characteristics, neighborhoods, and geographic data. Built and validated linear regression pipelines, improving cross-validation RMSE by approximately 5% and evaluating prediction bias across property-price segments.
Engineered keyword, formatting, and structural email features to build a logistic regression spam classifier with 90.2% training accuracy. Evaluated model performance and classification trade-offs using cross-validation, confusion-matrix metrics, probability thresholds, and ROC analysis.
Master's student preparing for a first professional role, focused on learning and hands-on projects.
I am Dalin, a graduate student at the University of Southern California pursuing a masters degree. I am early in my career and building practical experience while exploring directions for my first professional role.
Externships
Amazon Operational Strategy & People Analytics Externship
Amazon
Experience
Economics & Macroeconomics Tutor
UC Berkeley Student Learning Center · Jan 2026 – Present
Summer Equity Researcher | Extern
Lam Research Corp · Jun 2025 – Aug 2025
Student Secretary
UC Berkeley College of Computing, Data Science & Society · Aug 2024 – Jan 2025
Student Assistant
USAID & UC Berkeley Blum Center Program · Jan 2024 – Jan 2025
Lead Researcher | Undergraduate Research Apprentice Program
UC Berkeley · Aug 2024 – Dec 2024
Education
University of Southern California, Marshall School of Business
Master of Science in Business Analytics · Class of 2028
University of California, Berkeley
B.A. Economics (Honors), Minor in Data Science · Class of 2026
Skills
Analyzed 204,000+ property records and engineered predictive features from housing descriptions, building characteristics, neighborhoods, and geographic data. Built and validated linear regression pipelines, improving cross-validation RMSE by approximately 5% and evaluating prediction bias across property-price segments.
Engineered keyword, formatting, and structural email features to build a logistic regression spam classifier with 90.2% training accuracy. Evaluated model performance and classification trade-offs using cross-validation, confusion-matrix metrics, probability thresholds, and ROC analysis.
Analyzed 32,000+ San Francisco restaurant and inspection records by cleaning location data, identifying anomalies, visualizing patterns, and generating geographic restaurant recommendations.