Yash Jadhav I build machine learning models that quantify risk, detect fraud, and turn messy financial data into decisions. My work has supported $1M+ in portfolio decisions at Citi and processed 10K+ records at Stanford AIMI. At Citi, I developed XGBoost-based fraud detection models for a consumer credit portfolio - 99% recall on fraudulent transactions, 35% fewer false positives. I wrote the SQL pipelines that extracted features from millions of transaction records and built the reporting layer that translated model outputs into risk metrics leadership could act on. At Stanford AIMI (NYU Langone capstone), I led a data science team building NLP and machine learning pipelines for large-scale document analysis designing the data architecture, developing classification models, and improving detection accuracy by 42% across cross-functional teams. I graduated from NYU's MS in Data Science (Center for Data Science × Stern School of Business, GPA 3.8, May 2026). My coursework bridged quantitative finance and ML: Robo Advisors & Systematic Trading, ML in Finance, Probability & Statistics, Big Data, and Data Science for Business. Technical stack: Python, SQL, R, PySpark, Snowflake, BigQuery, XGBoost
AI Engineer (Graduate Capstone)
Business Analyst - Operations
Master's studies in science
Bachelor's studies in software
