• Enhanced filter panel functionality using C# .NET, MySQL, JavaScript, improving product search efficiency and contributing to a 20% increase in customer engagement and a 15% boost in customer acquisition. • Optimized Database performance by refining queries, resulting in a 30% reduction in load times and enhanced real-time filtering capabilities which helps user to easily search as per their requirements. • Improved user experience, enabling users to complete searches 70% faster and reducing the average number of clicks by 3, leading to higher customer satisfaction and reduced bounce rates by 25%. • Collaborated with cross-functional teams and given my valuable inputs to align development with customer needs, driving a 10%
Associate Software Engineer
Icertis
Feb 2022 - Dec 2023(1 year 11 months)
• Fetched thumbprint of SSL Certificate from various sources and mapped them all with grid on ICM tools website for comparison analysis it provides customers a platform where they can view subscription details. • Manages upkeep with automation team, raise bugs for multiple failed test cases applying thorough analysis. • Automated Multiple critical Hotfix Test Cases (client reported bugs) which helps organization to improve Quality and Functioning of Product and catches the bugs before going on production. • Tech Stack: CSS, HTML, JavaScript, C#, Dot Net, Azure, Selenium, XML, JSON.
Web Development Intern
SPADE EMS
Jun 2021 - Jul 2021(2 months)
• Worked on frontend of a live project and developed various features such as event page, social media corner • Implemented Features improved site enriching experience that exponentially increased click through rate. • Prepared a report that included all architectural changes and presented insights, generated through data visualization. • Tech Stack: CSS, HTML, JavaScript, ReactJS.
Education
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J
B. Tech Computer Science and Engineering
Jaypee Institute of Information Technology, Noida, UP
Projects (professional or personal)
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Used Car Price Prediction
NA
Jan 2022
• Predicted the price of a used car, using Sklearn supervised machine learning techniques integrated with Spark-Sklearn library and presented the correlation coefficient among independent variables. • Compared the accuracy of model by highlighting the R-square value calculated through linear and random forest regression on selected parameters having p value less 0.05. • Tech stack used: Python, Advance Excel, Scikit-Learn, Spark-Sklearn, Pandas, ELK Stack, Rfpimp.