MS in Applied Data Science

I’m a Master’s student in Applied Data Science at Indiana University (graduating Dec 2025) with hands-on experience in data engineering, statistical modeling, and analytics across automotive, healthcare, and research domains.

At Tesla, I worked on the Cell Qualification team where I built and optimized large-scale data pipelines to process 500K+ electrochemical test records weekly, integrated them with Battery Management System (BMS) firmware, and developed statistical models to detect internal shorts with 95%+ precision saving time, resources, and preventing defective cells from reaching end of life testing. I also implemented automated validation frameworks, cutting pipeline errors by 35% and reducing manual QA hours significantly.

Previously, I worked at Optum Global Solutions as a Data Engineer, designing ETL workflows and data processing solutions, and as a Research Assistant at Indiana University, contributing to data visualization and user interface projects for medical applications.

I enjoy solving complex problems with Python, PySpark, SQL, and cloud platforms like Azure Databricks, and I thrive in roles where data engineering meets analytics and machine learning. My goal is to leverage data to drive impactful, real-world decisions, whether it’s optimizing EV battery performance, improving healthcare outcomes, or building intelligent data products.