Work Experience

• Implemented data retrieval from OCI Database using Python and Shell scripting to power a Tableau dashboard, providing stakeholders with real-time business and system insights.

• Optimized and wrote over 80 SQL queries for data extraction workflows using the Oracle Data Integrator API, resulting in a 35% runtime reduction of the data pipelines, and implemented parameterized interfaces for scalability.

• Automated key business processes by writing 200+ JavaScript files, of scheduled and map-reduce scripts consisting of invoice generation, receipt creation, and others resulting in 30% faster performance in NetSuite operations.

• Interconnected data integration by developing 73 NetSuite's REST and SOAP API web services to external Power BI, Boomi, and Snowflake systems. This allowed system interoperability and facilitated data exchange with client teams.

• Led technical transitions for enterprise clients from the legacy NetSuite.com data source to NetSuite2.com, resolving complex field-mapping discrepancies and rewriting legacy SQL into optimized SuiteQL.

• Collaborated with Legal, Tech, and Operations teams to design and implement features, such as standarized feedback, automated dashboards, and user activity ensuring all departments had access to their required metrics.

• Managed performance for all digital tools by using SQL to identify data integrity risks and executing cleanup projects that maintained a 99.9% accuracy rate across all public legal resources.

• Presented weekly tableau dashboards on user activity metrics to senior management, providing data driven recommendations that led to a 12% increase in user engagement through targeted content updates.

• Applied Apache Hadoop’s MapReduce framework to parallelize the processing of 8 terabytes of academic data which reduced job completion times by 46% and it significantly improved data accessibility for research teams.

• Developed C++ algorithms using cached memory pools for specific data transformation tasks, significantly minimizing computational overhead and reducing memory bottlenecks which was causing performance regressions.

Education

• Achieved the highest grade in courses Data Mining (with 97%) and Data Cleaning (with 109%) among all enrolled students, excelling in data transformation, outlier detection, feature engineering, and clustering.

• Presented a machine learning project on predictive modeling using Scikit-learn's KMeans implementation at the UIUC Big Data 2025 Conference, showcasing insights into user engagement patterns.

• Coursework: Data Mining, Data Visualization, Applied Machine Learning, Natural Language Processing, Deep Learning for Healthcare, Cloud Computing Application, Scientific Visualization, and Theory and Practice of Data Cleaning.

• GPA: 3.87/4.00 (High Distinction), Dean's List of Scholar 2020-2023

• Coursework: Machine Learning, Neural Network and Deep Learning, Computer Vision, Algorithm Design and Analysis, SQL Databases, Data Structures and Analysis, Linear Algebra, Probability and Inductive Logic, Numerical Methods.

Projects

Certifications