Develop generative AI bot to communicate insights to AWS internal team from Amazon's in house data like documentation, blogs, FAQs. Leverage AWS services like AWS bedrock for Foundation LLM models and AWS Open Search Serverless as vector database, AWS S3 to store raw data from web. Streamline documentation access for AWS Technical Account Managers (TAMs), enhancing internal efficiency in troubleshooting support for enterprise clients’ multi-product service configurations.

Led key projects that significantly impacted business outcomes. Analyzed sales pipeline trends, boosting conversions and growth through regional analyses and strategic events. During the COVID-19 pandemic, provided crucial insights on deposit fluctuations using Alteryx and Tableau, and identified upselling opportunities in card purchases. A study on digital and manual bank products led to a 15% increase in sales through strategic cross-selling. Additionally, spearheaded the automation of COVID-19 analytics, saving 1,800 man-hours annually and ensuring data quality.

Engineered a Power BI interactive dashboard with live visualizations, processing real-time data from three different tools through Python. Developed a highly accurate (90%) classification model using NLP TF-IDF and statistical analysis for virtual ticket dispatching, reducing annual man-hours by 28,800. Implemented a CART model to classify and address incidents in real-time, significantly improving response efficiency. Conducted a deep dive into ticketing data for seven clients using Power BI, Java, and Python, yielding meaningful and actionable insights that enhanced infrastructure health by 23%. Participated in an IBM AI hackathon, predicting household electricity consumption using a four-year historical dataset and ARIMA for Time Series Forecasting.