Centralized, cleaned, and standardized multi-year survey datasets for pre-experience programs into a unified database to support reliable longitudinal analysis. Performed data quality checks, deduplication, validation, and schema standardization across heterogeneous sources. Collaborated with academic teams to translate analytical requirements into structured, analysis-ready datasets, improving data consistency and reusability.

Built and optimized SQL-based ETL pipelines integrating business and transactional data to ensure data integrity and reliable analytics. Developed machine learning-based customer segmentation models using Python, reducing churn by 20%. Designed automated Power BI dashboards to track key metrics, improving reporting efficiency by 30% and supporting sales and marketing decision-making.

Developed an OpenCV-based automation framework that improved grading efficiency by 40% and reduced manual errors. Built and evaluated machine learning models to support automation use cases, including data preprocessing, model validation, and performance analysis. Collaborated with BI teams to integrate AI-driven analytics into existing workflows.

Designed and validated a Customer Lifetime Value (CLV) prediction model using Python and SQL, focusing on feature engineering and model performance. Extracted, cleaned, and analyzed customer data from relational databases to generate insights for customer retention strategies and presented findings to stakeholders.