Developed a Python-based automation tool integrating SQL queries to streamline real-time product analytics, successfully enabling daily email updates to stakeholders. Spearheaded the development of a Churn Prediction Model aimed at identifying users with a high likelihood of not engaging in transactions in the next month. This project involved end-to-end development, starting from feature exploration and selection, extensive exploratory data analysis (EDA), and building models using Random Forest, XGBoost, and Logistic Regression.Managed marketing campaigns such as the leaderboard campaign from the data side, which included rank calculation of users based on transactions on specific deals, coordination with tech team for fetching users data and sending ranks data, campaign analysis, and campaign validations.