• Focus on applying data science, machine learning, and business intelligence to work • Build data pipeline for requirements via Apache Airflow • Data ETL - Deal with > 10 million records/GB level of daily server logs and market data • AWS Services - S3, Athena, SageMaker, Neptune, Kinesis, Personalize, Rekognition, and so on • Data Visualization/Analysis/Report - Apache Superset, Tableau, Google Analytics - ad-hoc request with Jinja template • Data Warehouse, OLAP - Apache Kylin • Machine Learning - MLOps - MLflow - SOTA paper survey, implementation and deployment - Domain: Recommendation system, Graph neural network, Computer vision • Build 3 data products from scratch (1) Content retrieval system - Deep learning model to extract content information from templates, automatically tagging to template - Huge improvement of template exploration and user experience after conducting statistic test (Improved by over 30%) (2) Personalized recommendation system - Launch on Promeo - App launched by CyberLink - A from-scratch MLOps based recommendation system that automatically and steadily train, choose, and deploy model then inference - Pass real-world performance test via A/B test that have a conversion rate enhancement by over 35% (3) Graph neural network based Customer Data system - Construct GNN-based Persona and conduct customer segmentation - Model users and their interaction and relationship into graph structure - Explore the implicit preference of customers with the help of GNN