Tejaswini S Email & Phone Number

Chicago, IL, US

• Utilized SQL and Python for data extraction, cleaning, and analysis; conducted exploratory data analysis (EDA) to identify key metrics and trends that guided business strategy.
• Created predictive models using machine learning algorithms to forecast sales and customer trends, achieving a 15% increase in forecast accuracy.
• Conducted complex statistical analyses, including regression and clustering, to uncover insights and trends that informed strategic business decisions and increased operational efficiency by 25%.
• Collaborated with 10+ cross-functional teams to analyze business objectives and deliver tailored data-driven solutions, improving operational efficiency by 20%.
• Developed custom scripts and utilized Python, SQL, and R tools to automate data collection, cleansing, and transformation tasks.
• Conducted a comprehensive data analytics of client behavior by leveraging Python libraries to develop insightful reports using sales data from 30+ geographies.

India

• Utilized tools such as SQL, Python, and R for data extraction, cleaning, and analysis; created dynamic dashboards and visualizations using Power BI and Tableau to support data-driven decision-making.
• Designed and maintained complex databases in SQL Server and MySQL; optimized queries for performance improvements, reducing data retrieval times by up to 40%.
• Streamlined data processing workflows by implementing automation scripts in Python, reducing manual effort by 30% and increasing overall efficiency.
• Pioneered the implementation of custom data integrations which involved creating complex stored procedures and utilizing Common Table Expressions (CTEs) for optimized data loading processes, significantly improving the.
• Established and maintained proactive communication channels with key customers, understanding their business needs and recommending tailored data management and analysis solutions using modern data warehousing tool.
• Created and optimized relational and NoSQL databases using MySQL, PostgreSQL, and MongoDB, resulting in enhanced data retrieval and processing speeds.