Data Analyst Business Intelligence

Meghana Tiyyali

Spire street, Hayward, CA - ***** +1-510-***-**** ******************@*****.*** Meghana Tiyyali

Professional Summary

A data-driven Engineering Management Master's graduate with 4 years of experience in statistical analysis, data visualization, and problem-solving using Python. Known for ensuring data accuracy and reliability, and providing recommendations for improved business growth and operational efficiency.

Education

Master of Science in Engineering Management California State University Eastbay, California - USA January 2023 – Present

Bachelor of Science in Computer Science Engineering J.N.T.U.K – India June 2018 – June 2022

Area of Expertise

Business Intelligence Tool Tableau, Microsoft Power BI, MS Project, MS Excel.

Language SQL, Python (Pandas, NumPy), R, C++.

Web Development HTML, CSS, JavaScript

Methodology Agile, Waterfall, Kanban.

Professional Experience

DATA ANALYST Intern Gieco – California January 2024 – Present

●Developed a predictive model using random forest regression with 97% accuracy to identify high-risk customers, potentially saving Geico $7,000 annually on claim payouts.

●Spearheaded Python's NumPy and Pandas modules, which improved data quality measures by 95% and led to a reduction of 50,000 data errors found in insurance datasets.

●Implemented A/B testing using Python on a new claims processing workflow identified through analysis, which resulted in an average claim processing time reduction of 5 days.

●Analyzed Customer Feedback and Recommended Changes that Reduced Customer Churn by Thousands Per Year, Benefiting Geico's Customer Base and Revenue

DATA ANALYST Google – India August 2022 – November 2022

●Analyzed consumer data using Scikit-learn python library to determine purchasing patterns and guided marketing initiatives that led sales throughout the thermostat product line to increase by 18% and customers increased by 12% as a result of this strategy.

●Leveraged Tableau to reduce production inefficiencies by 20%, resulting in 90,000 thermostat quality improvements.

●Implemented a demand prediction model using Python language that achieved 99.7% accuracy, which resulted in a significant reduction of 40% in stockouts and a decrease of 25% in excess inventory. These improvements eventually led to substantial cost savings of $500,000 for the company.

●Explored the production data to identify cost-saving opportunities and discovered that material usage and process improvements could reduce costs by $3 per unit. This led to a 15% increase in profit margin for the product line.

●Utilized process mapping to analyze internal operations, detect bottlenecks, and suggest data-driven solutions. These modifications led to a 25% increase in project completion rates, fostered team collaboration, and improved efficiency.

●Created a strategy to group consumers interested in high-value thermostats, with qualified leads of 30% and a 15% conversion rate jump, which led to a potential sales increase of 180 units.

Academic Projects

Real-Time Object Detection with Voice Assistant Integration

●Engineered a Python-based real-time object detection system using YOLO technology to identify essential station elements like license plates and fuel tanks with a 92% precision rate to assist visually impaired individuals, helping them navigate independently and reducing their reliance on others.

●Integrated a natural language processing module to enhance functionality, transforming the system into a scalable virtual assistant for mobile applications.

●Conducted statistical analysis using Python, and identified significant trends and patterns within research datasets.

●Supported the development of predictive models and algorithms, leading to a notable 15% improvement in model accuracy.

●Increased efficiency rate by 5% through meticulous data entry and verification tasks for accurate and complete research datasets.

●Contributed to the development of comprehensive project reports and presentations, summarizing research findings and providing recommendations for diverse audiences.

Maximum Solar Energy Forecasting using Machine Learning

●Created a regression model utilizing Machine Learning techniques for solar energy forecasting, leveraging weather conditions as input features.

●Implemented algorithms including RF, SVRM, and LR, resulting in a 95% data accuracy rate.

●Provided a user-friendly web format to present the output, accompanied by detailed project documentation.

●Proposed and evaluated RF and LGBM models for PV solar energy output forecasting, identifying the most effective algorithm with a 98% prediction accuracy rate.

Achievements

●Certified in diverse programming languages, including Python, Java, SQL, C, and C++

●As the student coordinator for GYAAN 2K22, I organized events and won first place in a mathematical quiz competition.

●Certified in Data Structures and Algorithms, Udemy.

●Google Certified in Fundamentals of Digital Marketing, Google

●Certificate of completion on Master Tableau in Data Science, Udemy

Volunteer Works

•I hold the position of event head for the Hindu YUVA Organization.

•Volunteered for World Space Week, an event meticulously organized by the Indian Space Research Organization (ISRO) Satish Dhawan Space Centre (SDS) SHAR. It was a fantastic chance to contribute to the worldwide observance of space exploration and creativity.

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