Mission:
Lead Data Science for Ads to maximize ads effectiveness through recommender systems, user targeting, and large-scale text & behavior modeling, directly driving revenue growth.
Key Responsibilities:
1. Modeling & Algorithms
- Own ads recommender systems;
- Lead user targeting models;
- Master problem modeling, feature engineering, and model optimization for multi-objective, goals: CTR, CVR, eCPM, revenue, and user experience.
2. Feature Engineering & Data Mining
- Efficient data mining & prediction at scale: 80M+ users, 2K+ features, 20K+ items;
- Build reusable feature sets for ads ranking, targeting, and personalization;
- Extract semantic signals from text data (items, creatives, user activity).
3. Platform & Data Foundation (Optional / Plus)
- Master Data/ML Platform: Data Lake & Data Warehouse, Hadoop, Apache Spark, Airflow, Kafka, SQL optimization, MLflow, Docker/Kubernetes;
- Ensure scalable pipelines from data model online serving: 80M+ users, 10K+ RPS.
4. Leadership & Collaboration
- Lead and develop DS/DA/DE teams (25 members);
- Partner closely with Ads Product, Engineering, and Business to turn models into revenue impact;
- Own end-to-end delivery: problem framing modeling A/B testing business results.
