- Conducted exploratory data analysis (EDA) to identify behavioral patterns, anomalies, and actionable insights from structured financial datasets. - Engineered domain-relevant behavioral KPIs to transform raw transactional attributes into interpretable analytical features for decision-making. - Applied data preprocessing techniques including outlier handling, feature scaling, skewness correction, and missing value treatment to improve analytical robustness. - Utilized unsupervised machine learning and statistical validation techniques to identify meaningful behavioral segments within customer data. - Developed insight-driven visualizations and analytical summaries to communicate customer behavior patterns and support business-focused recommendations. - Translated analytical findings into strategy-oriented insights related to customer engagement, retention, and risk behavior. - Worked with Python libraries including Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn to build end-to-end analytical workflows and data-driven reports.