Winston Mo
ML AI Product Leader | Driving Engagement for 25MM+ Monthly Active Users by enhancing content recommendations and personalization for Glassdoor Community
- Role
- Principal Product Manager, Technical - Machine Learning & Applied Ai at Glassdoor
- Location
- San Diego, CA, US
- LinkedIn followers
- 500 followers
About Winston Mo
Hi, I’m Winston! The Principal Technical Product Leader driving content…
Experience
Principal Product Manager, Technical - Machine Learning & Applied Ai
Mar 2023 — Present · Dallas, TX, US
Report directly to Product VP into the Chief Product Officer (CPO).Leadership:• Executed product portfolios for Content Recommendation lane and the ML vertical of Community lane• Led strategic vision, crafted product roadmaps, and coordinated product strategies across adjacent product groups, 3.5x engaged community users in 1.5 years, consistently beating quarterly goals by 7 - 10%• Supervised 2 technical PMs for Content Understanding and User Understanding as their player-coach, improving team YoY output by 20+% via structured performance feedback and guidance• Formulated company-level ML Product Strategy and OKRs, improving ML-org delivery KR impact by 20+%• Headed Product & Design org’s culture improvement initiative, decreasing burnout by 8% over 6 months• Bridged PDLC gaps between ML and Core product teams, reducing ML project abandonment by ~15%Product:• Pioneered 4-stage ML Recommendation System, 5x engagement for 25 Million active users in 1 year• Adapted ML Feed Recommendation for email and push notification, boosting app session opens by 90+%• Directed Name Entity Recognition ML development, lifting engagement by 18 - 122% for 6 partner teams• Launched Generative AI (ChatGPT) powered Content Seeding and Push Notification Editorialization, improving seeded content engagement by ~35% and notification click-through rate by 20+%Product - ML Recommendations System Details:• ML Science: Productionized Sentence Transformer embeddings, user embeddings, XGBoosted DT Ranker, DNN Ranker, 2-tower DNN, and candidate generation graph layer, collectively 5x engagement for 25 Million active users in 1 year• ML Platforms: Productionized ML model registry, ML control center, ML feature stores, Vectorized Search (ANN) with OpenSearch, Embedding-Based Candidate Generation (EBCG), 4-stage Recommendations platform, and rapid experimentation framework, reducing 95th percentile (P95) latency/response time by 20+% and deployment/maintenance overhead by 60+%.
Education
University of Toronto
Bachelor of Applied Science - BASc
Georgia Institute of Technology
Master of Science - MS
University of Toronto - Rotman School of Management
Bachelor’s Degree
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