Summary
Boyue Wang is a data scientist with nine years of hands-on experience turning complex datasets into actionable business and clinical insights, currently based in Los Angeles. He has led end-to-end projects from multiomics risk modeling—developing Random Forest and XGBoost models that achieved 88% accuracy and a 98.72% AUC for diabetes prediction—to building automated BI pipelines and forecasting dashboards at scale using Redshift, S3, Athena, QuickSight and Tableau. Comfortable across SQL, Python, pandas and feature-selection techniques like PCA and RFE, he bridges domain experts and stakeholders to translate technical results into clear visual reports and operational improvements. Past work includes large-scale consumer behavior analysis for e-commerce brands and insurance pricing analytics from millions of medical records, reflecting a strong mix of product and research impact. Notably, he reduced manual dashboard refresh time and uncovered a 50% revenue discrepancy at Amazon through careful data validation and ETL automation—evidence of his penchant for hunting down hidden data issues.
9 years of coding experience
2 years of employment as a software developer
Master's degree Industrial & System Engineering-Analytics, Master's degree Industrial & System Engineering-Analytics at University of Southern California
Bachelor's degree Mathematics - Statistics track, Bachelor's degree Mathematics - Statistics track at University of Maryland
English, Chinese