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Promotion Hub: Voucher Ranking 🎰 Implemented real-time Multi-Armed Bandits (MAB) framework to optimize the promotion recommender system at scale for a user base of 40 million, utilizing Bisecting K-Means clustering model and Thompson Sampling algorithm. 📈 Conducted rigorous A/B tests, leading to an impressive 18% increase in click-through rate (CTR) and a 3% boost in user engagement, demonstrating the effectiveness of the implemented MAB strategies.⚡️ Developed and maintained end-to-end data pipelines, encompassing data collection, cleaning, and preparation at scale, while ensuring seamless integration with real-time data streams from Kafka, and real-time serving with HBase.

Promotion Platform: Voucher Optimization💰 Developed an organic model leveraging advanced machine learning techniques to accurately identify non-promotional users, resulting in a substantial cost savings for the company.👨‍💼 Spearheaded the platformization strategy for the organic model, facilitating its integration across various business units. As a result, 15 different business unit use cases were successfully implemented, further enhancing operational efficiency and decision-making processes.

Deal Market: 🏪 Implemented a popularity-based recommendation algorithm aimed at enhancing user engagement and conversion rates within the Deal Market platform, successfully boosting user conversion by 15% through personalized user experiences.📟 Leveraged a diverse tech stack including BigQuery, DataProc, Airflow, HBase, Kubernetes (K8s), deployed solutions using Docker, Jenkins and ArgoCD, and monitored the system by Promotheus, and Grafana, facilitating efficient and scalable data processing and model deployment.

Teaching Assistant of the subject "Statistical Methods" in semester 2 of the academic year 2019-2020.