Heather Huiyu Song
Machine Learning Engineer @DoorDash
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WORK HISTORY
Machine Learning Engineer @DoorDash
Ads Quality Team• Launched multi‑label multi‑task (MTML) DNN models for Ads ranking, achieving a 0.85% revenue gain and addressing online‑offline AUC gaps.• Developed a Grocery Ads LightGBM model, later upgraded to an MTML DNN model, resulting in 2.52% and 2.51% revenue gains, respectively.• Built auto‑calibration pipelines (Logistic Regression, Platt Scaling, Polynomial Regression) to enhance auction fairness across all Ads models.• First integrated Homepage Banner rankers into a multi‑armed bandit (MAB) platform, improving CTR by 4% and addressing attribution gaps.• Optimized the Order Value Prediction model for auto‑bidding, reducing Cost Per Action (CPA) by 4.63% and prediction error by 7.8%.• Enhanced Search Ads models by incorporating context and price‑sensitivity features, increasing restaurant search ads revenue by 1.6%.• Identified year‑long broken Search Ads feature pipelines during on‑call, implementing fixes that led to +0.50% CVR and +1.55% revenue gains.• Mentored junior Machine Learning Engineers on the Ads team through 1:1 supervision for feature engineering and model development.
EDUCATION
Tongji University
Bachelor of Engineering - BE, Surveying Engineering
Columbia University
master of data science, Data science
ABOUT HEATHER HUIYU SONG
As a Machine Learning Engineer at Doordash, I have been working on Ads Ranking models E2E in past two years. I am interested in Recommendation System.
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