Aayush Jha

· Led multi-platform e-commerce integration by registering and setting up the brand presence on Zonate, Swiggy, Amazon, Flipkart, and Blinkit, ensuring full compliance with marketplace guidelines and enabling seamless digital visibility.

· Revamped product listings and optimized content across major platforms (Zomato, Swiggy, Amazon), resulting in increased product visibility, improved discoverability, and a measurable uplift in customer orders.

· Supported cross-functional operations, including order fulfillment coordination, packaging design collaboration, and process optimization to enhance customer satisfaction and reinforce brand consistency in digital and physical touchpoints.

· Resolved Complex Technical Issues: Diagnosed, debugged, and resolved software bugs, system anomalies, and user-reported incidents, ensuring high application availability and improved user experience across platforms.

· Ensured Code Quality and System Stability: Conducted thorough code testing and quality assurance to validate functionality, minimize deployment risks, and maintain overall software reliability through proactive identification of potential defects.

· Database Administration and Operational Support: Managed and optimized database performance through regular maintenance, backups, and security protocols, while handling service requests to ensure seamless IT operations and data integrity.

· Cross-functional Collaboration and Documentation: Partnered with development, infrastructure, and support teams to address technical challenges, streamline workflows, and maintain comprehensive documentation of system configurations and issue resolutions.

· Developed an automated news categorization system using Natural Language Processing (NLP) techniques to classify articles into predefined categories, enhancing content organization and retrieval.

· Implemented and evaluated machine learning models including Naive Bayes, Support Vector Machine (SVM), and Random Forest, achieving high classification accuracy using metrics such as precision, recall, and F1 score.

· Performed end-to-end text preprocessing and feature engineering, including tokenization, lemmatization, stop-word removal, and Bag of Words vectorization, while addressing scalability and ambiguity challenges in large datasets.