Sai Pranav Madineni
Ai Engineer (Capstone Project at Carnegie Mellon University) @Virtual Gold
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WORK HISTORY
Ai Engineer (Capstone Project at Carnegie Mellon University) @Virtual Gold
Pittsburgh, PA, US
Prototyped an enterprise-grade AI assistant for CEOs, integrating multi-agent workflows (CrewAI) and retrieval-augmented generation (LangChain + GPT), enabling executives to cut manual analysis time by 65%Engineered RAG pipelines with FAISS retrieval, boosting contextual accuracy by 40% for financial and strategic decision supportCombined structured (finance, sales) and unstructured (reports, filings) datasets into automated insights dashboards for senior stakeholders
EDUCATION
PES University
Bachelor of Technology - BTech, Computer Science
Carnegie Mellon University
Master of Information Systems Management - Business Intelligence and Data Analytics
ABOUT SAI PRANAV MADINENI
Master’s student in Information Systems Management (Business Intelligence & Data Analytics) at Carnegie Mellon University | Data Science & ML Enthusiast I’m passionate about turning data into impact through machine learning, predictive modeling, and business intelligence. With hands-on experience in Python, SQL, R, and cloud platforms, I specialize in building scalable, data-driven solutions. Currently, I’m a Data Scientist Intern at Amazon on the Stores Finance team, where I’m working on time series forecasting, trend detection, and data-driven insights to support financial planning and strategy. It’s been a rewarding opportunity to deepen my knowledge of statistical modeling and AWS-based analytics. Previously at Société Générale, I worked as a Full Stack Developer, building scalable software solutions and integrating backend services with frontend interfaces for financial applications. My project work includes BERT-based sentiment analysis (99% accuracy), stock price forecasting models, and churn prediction—leveraging NLP, deep learning, and structured data analytics to solve real-world problems. Lately, I’ve been diving into Large Language Models (LLMs), exploring their applications in NLP, text generation, and business insights—focusing on fine-tuning, inference optimization, and enterprise adoption. Always open to exchanging ideas and learning from others in the field.
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