Sai Samhita Pusuluri

Master's

Interdisciplinary Computer Science

MetLife

Data Analyst

- Implemented SDLC methodologies, including Waterfall, to configure and advance processes, ensuring strict compliance with industry - Spearheaded the conception and execution of a comprehensive data analytics solution for an e-commerce client, showcasing proficiency in Python, SQL, and AWS services, as well as PySpark for scalable data processing - Utilized PySpark alongside Pandas, NumPy, and Scikit-learn for advanced data manipulation, analysis, and machine learning applications - Led a team in designing and implementing a highly efficient ETL pipeline, resulting in a substantial 25% reduction in data processing time - Applied cutting-edge analytics and statistical models to discern customer behavior patterns, contributing significantly to a 20% increase in the efficacy of personalized marketing strategies - Demonstrated Tableau proficiency to craft dynamic dashboards, providing real-time insights into sales performance and optimizing inventory - Utilized Power BI, in addition to Tableau, for creating intuitive and impactful reports, ensuring diverse tools for data visualization - Conducted regular data profiling and meticulous quality checks, ensuring the utmost accuracy and completeness of the dataset - Collaborated seamlessly with cross-functional teams to define key performance indicators (KPIs) and enhance reporting processes, fostering - Achieved a noteworthy 15% improvement in reporting efficiency through the implementation of streamlined data processes - Oval SoftTech, India - Data Analyst - Orchestrated project deliveries within a Waterfall framework, overseeing adaptive and phase-based implementation strategies - Utilized R and SAS for data hypothesis testing, contributing to the refinement of data quality - Analyzed user interactions to enhance customer communication strategies, fostering an increase in user engagement - Created dynamic data visualizations using Power BI and QlikView to support business growth initiatives - Executed root cause analysis through TensorFlow and Keras, identifying strategies for performance improvement - Enhanced SQL and Apache Kafka configurations to optimize data streaming and retrieval, resulting in increased data access efficiency - Led data-related initiatives, encompassing collection, cleaning, wrangling, and machine learning model development - Collaborated with stakeholders to define data requirements, ensuring a high satisfaction rate in delivered reports - Leveraged Alteryx for advanced data blending, promoting harmonization across diverse data sources - Conducted data literacy training sessions, elevating the data proficiency of non-technical teams