Rikvitha Madulapally

• Analyzed 50K+ banking and financial transaction records using SQL and Python to support operational and compliance-oriented reporting. • Validated, cleaned, and standardized transactional data, reducing data inconsistencies and manual corrections across recurring reports. • Conducted exploratory analysis to identify anomalies, trend shifts, and data-quality gaps in financial datasets. • Built structured analytical reports and dashboards summarizing key operational metrics for internal review. • Documented validation logic and analysis workflows to support audit-ready, repeatable reporting processes

• Analyzed 100K+ e-commerce records (orders, customers, products, payments) using Python and SQL to identify sales trends and customer behavior patterns. • Built data cleaning and feature-engineering pipelines (conversion rates, rolling averages, repeat-purchase metrics), reducing analysis time by ~40%. • Performed exploratory data analysis to uncover demand seasonality, product outliers, and revenue concentration across categories. • Developed baseline machine-learning models for customer segmentation and demand trend prediction using scikit-learn, improving insight accuracy over manual analysis. • Created interactive dashboards and visual reports in Power BI and Python, supporting business-style decision reviews and project presentations.

Responsible for internal and external publicity, strategizing work-plans and executing them, CSI is the committee that gives one a hands on experience of working in the corporate world with big teams and even bigger ideas.