Sai Nikhitha Penugonda

In my role at Ionic Technologies, I analyze and interpret large datasets to identify key business trends, patterns, and insights that support strategic decision-making. I develop interactive dashboards and data visualization reports using Tableau, Power BI, and Excel, enabling stakeholders to effectively monitor key performance indicators and overall business performance. I write and optimize complex SQL queries to extract, clean, and transform data from relational databases for analytical and reporting purposes. I also build automated data processing workflows using Python, SQL, and ETL tools to improve efficiency and reduce manual data handling. Additionally, I apply statistical analysis and predictive analytics techniques to generate actionable insights that help drive data-driven strategies. I ensure data accuracy and integrity by implementing structured validation and data quality checks. My role also involves collaborating with cross-functional teams to understand business challenges and deliver analytical solutions, while translating complex data findings into clear reports and presentations for stakeholders. I continuously explore new analytical tools and industry trends to improve data analysis processes and support organizational performance.

As a Student Assistant for General Chemistry Stockroom at University at Buffalo, I provided hands-on support to students by helping them locate and safely handle chemicals, ensuring they had the necessary materials for their experiments. I assisted in explaining proper lab safety procedures, ensuring compliance with regulations, and guiding students in understanding chemical storage and handling protocols. By maintaining an organized and accessible stockroom, I contributed to a smoother lab experience, allowing students to focus on their experiments without disruptions.

Data Analyst | Sudharshan Infotech December 2022 – July 2023 As a Data Analyst at Sudharshan Infotech, I played a key role in driving data-driven decision-making across sales, supply chain, and production. Using Excel and Power BI, I analyzed customer purchase trends and market demand, leading to a 15% increase in sales by identifying top-performing product designs and target demographics. Leveraging SQL, I optimized supply chain operations, reducing material procurement costs by 12% and minimizing inventory waste. I also developed interactive dashboards to track real-time production metrics, enhancing manufacturing efficiency and strategic planning. Additionally, I utilized Python and machine learning models to forecast seasonal demand patterns, effectively reducing stockouts and excess inventory while ensuring optimal production planning.

Master of Professional Studies (MPS) in Data Science at University at Buffalo, where I developed a strong foundation in data analytics, statistical modeling, and machine learning. The program focused on analyzing complex datasets and transforming data into meaningful insights to support data-driven decision-making. Through hands-on coursework and projects, I gained practical experience in Python, SQL, data visualization tools such as Tableau and Power BI, predictive analytics, and large-scale data analysis, strengthening my ability to solve real-world business problems using analytical and data-driven approaches.

Bachelor of Science (B.Sc.) in Mathematics, Computer Science, and Statistics from St. Francis College for Women, where I developed a strong foundation in analytical thinking, quantitative reasoning, and computational problem-solving. The program provided in-depth knowledge of mathematical concepts, statistical analysis, and programming fundamentals, helping me build the ability to analyze data, interpret patterns, and approach complex problems with logical and structured thinking. This academic background laid the groundwork for my continued interest in data analytics and data science.