Kunlong Wu

Led a data-driven initiative analyzing a 3.8M-row, 52-column dataset to address 15 complex business questions focused on customer acquisition, retention, and competitive positioning. Built predictive models in Python to identify patterns in customer behavior and optimize product strategies across life, health, and supplemental insurance offerings. Delivered actionable insights through interactive visualizations, comparing Prudential's performance with key competitors, uncovering retention drivers, and highlighting growth opportunities. Collaborated cross-functionally to iterate on models and align data solutions with strategic business goals.

Designed and implemented an automated data pipeline using Python and SQL to extract, process, and analyze job information, enhancing data accessibility and accuracy. Optimized information retrieval workflows, reducing processing time from 2 hours to minutes, significantly improving operational efficiency. Applied Agile methodologies and leveraged Trello for project tracking, improving project coordination, time management, and quality assurance.

Analyzed 500,000+ rows of transactional data using Python, Oracle SQL, and R, uncovering sales trends and customer insights to evaluate a new product line. Built an interactive Tableau dashboard, enabling stakeholders to visualize key performance metrics and make data-driven decisions. Designed optimized SQL queries and used Python (Pandas, NumPy) for data transformation, aggregation, and statistical modeling to assess product performance. Delivered strategic recommendations based on data analysis, influencing adjustments to the product line and improving business strategy.