Hyderabad, Telangana, India
Data Science and Business Analytics professional with hands-on experience in revenue and retention-focused analytics using SQL, Python, and Machine Learning. Completed a 6-month Data Science internship working on financial risk prediction and NLP-based sentiment analysis, handling data preprocessing, feature engineering, model development, and performance evaluation on real-world datasets. Built end-to-end analytics and ML systems focused on revenue risk modeling and customer retention insights, transforming raw data into structured, decision-ready intelligence for business impact.
Designed and implemented a relational database schema to simulate real-world customer, order, payment, and support interaction data. Performed advanced SQL analysis using CTEs, window functions, joins, and aggregation techniques to evaluate revenue concentration, customer lifetime contribution, and churn risk patterns. Developed structured churn detection logic by analyzing declining purchase frequency, payment failures, and support interaction trends. Segmented customers into retention-priority and upsell-opportunity cohorts to support targeted revenue protection and growth strategies. Translated analytical outputs into business-focused insights aimed at improving customer retention and revenue stability.
Worked on supervised machine learning solutions for financial risk assessment and text-based sentiment classification using structured and unstructured datasets. Designed and implemented bankruptcy prediction models using Logistic Regression and Random Forest, performing data preprocessing, missing value handling, feature engineering, and multicollinearity analysis to improve model reliability. Conducted model evaluation using classification metrics including accuracy, precision-recall, and confusion matrix analysis to validate predictive performance. Developed an NLP-based sentiment analysis pipeline using TF-IDF vectorization and supervised learning algorithms to extract structured insights from textual datasets. Collaborated within a team environment to iterate on model performance and ensure analytical outputs aligned with defined problem objectives.