● Analyzed funds across 15 AMCs with 500,000 investors, leveraging PCA and k-means clustering to identify six critical cases for account recovery. ● Reported RFM scores and transitioned from rule-based to data-driven practices, enhancing the decision-making process for unclaimed funds and boosting redemption rates by 20%.

● Conducted A/B testing, hypothesis testing, and regression analysis to analyze unclaimed funds in Indian mutual funds, identifying investor trends. ● Developed SQL queries to extract and analyze financial data, and built data-driven models and visualizations using Python to support fund recovery strategies and policy recommendations.

● Led the end-to-end development of cloud-based machine learning pipelines for an FMCG company using Azure Data Factory, and Azure Databricks, while collaborating with cross-functional teams including SMEs and business units. ● Developed and deployed classification and forecasting ML models tailored to client-specific requirements using Azure Machine Learning and boosted sales by 7% by providing analytical reports using SQL and Python. ● Developed an automated phone number extraction workflow using OCR technology for a UK based client, enriched with insights from AWS SageMaker, and deployed via AWS Lambda. ● Achieved a 30% reduction in processing time and a 15% improvement in accuracy, streamlining extraction processes and enhancing operational efficiency.

• Developed document classification NLP model for 150,000 PDFs, achieving a 12% accuracy boost using BERT. • Streamlined workflows, reducing processing time by 15%. • Augmented dataset with additional metadata for improved analytics and insights.