Sreeja Potnuri

About Sreeja Potnuri

I am a passionate and results-driven Data Analyst with a strong background in leveraging data-driven insights to optimize business operations and financial outcomes. With experience working at top organizations like JPMorgan Chase & Co and IBM Corporation, I specialize in building and optimizing ETL pipelines, predictive models, and cloud-based solutions to drive efficiency and revenue growth. At JPMorgan Chase & Co, I led initiatives that improved data processing efficiency by 35%, increased sales forecasting accuracy by 20%, and reduced AWS costs by 18%. I also designed financial models that enhanced decision-making speed by 30%, and helped reduce customer churn by 12% through targeted retention strategies. Earlier in my career at IBM, I was able to optimize data extraction processes, automate reporting tasks, and improve supply chain operations, resulting in a 20% ROI increase and a 15% reduction in waste. I’ve worked with a wide range of technologies, including Python, R, SQL, PowerBI, Tableau, Azure, and AWS, as well as big data tools like Spark and Hadoop. My expertise spans across financial analysis, data engineering, machine learning, and project management, and I am always eager to apply my skills to help organizations make data-driven decisions and solve complex business challenges. Feel free to connect with me to discuss potential collaborations or opportunities!

Experience

1. Data Analyst

   JPMorganChase

   Mar 2024 — Present · Madison, WI, US

   Conduct comprehensive project analyses, identifying operational gaps and implementing data-driven solutions toalign with financial objectives. Built and optimized ETL pipelines for large datasets, improving data processing efficiency by 35%.Developed predictive models using Python, increasing sales forecasting accuracy by 20% and driving a 15% increasein revenue. Automated data extraction processes, reducing manual reporting time by 40% and enabling real-time insights viaTableau dashboards.Reduced AWS costs by 18% through efficient resource monitoring and optimization of cloud-based data storage.Designed and implemented financial data models, improving forecasting accuracy by 25% and acceleratingdecisionmaking by 30%.Integrated disparate data sources into a unified platform, reducing data retrieval time by 40% and enhancing overalldata integrity.Reduced customer churn by 12% through predictive modeling and targeted retention strategies using machinelearning algorithms. Optimized SQL queries for data extraction, reducing query processing time by 35% and enabling faster reporting.

Education

• Suresh Gyan Vihar University

  Bsc agriculture

  2016 — 2020

• University of Wisconsin-Madison

  Master of Science - MS

  2022 — 2024