6+ years of experience as a Data Scientist with proven success in building predictive models and algorithms across diverse industries. Proficient in Regression, Classification, Text Mining, and Generative AI techniques, with expertise in leveraging Large Language Models (LLMs) for advanced insights and decision-making. Skilled in data analysis, visualization, and applying Machine Learning strategies to tackle complex real-world challenges. Adept at developing algorithmic solutions and engineering applications that optimize business operations and drive efficiency. Recently, I have been integrating Generative AI (GenAI) and Large Language Models (LLMs) to extract and summarize key insights from data, making it actionable and relevant to business goals. Highlights: ‾‾‾‾‾‾‾‾‾‾‾‾ 1.Enhanced investor transcript analysis: Built an LLM-powered solution for investor call transcript analysis to extract critical financial insights, which improved decision-making processes by highlighting key themes and trends. 2.Streamlined SOW contract negotiations: Applied natural language processing (NLP) techniques to automate SOW contract review, saving valuable time and ensuring accuracy in contract terms. 3.GenAI and LLM skills: Designed QA chatbots for real-time, context-aware responses based on extensive document and transcript data. Engineered LLM-based solutions for summarizing lengthy documents, enhancing information retrieval efficiency. Integrated Google Generative AI and Azure OpenAI models to provide insightful, nuanced answers based on semantic understanding rather than keyword matching. 4.Transformed supply chain efficiency: Built a 95% accurate OTIF forecasting model, reducing on-time delivery delays by 12%. Implemented automated STO optimization, saving 1.75 hours daily for the deployment team. 5.Increased sales and reduced waste: Developed an AI-powered order recommender engine, boosting revenue by 10% through personalized cross-selling and upselling. Reduced inventory waste by 5% through obsolescence prediction. 6.Streamlined operations and saved costs: Automated STO processes with custom algorithms, eliminating manual tasks and saving $X annually. Implemented load balancing solutions, optimizing resource utilization by 15%.