Experience

Karomas Aromatics

• India
• Wholesale Chemical and Allied Products
• 1 - 100 Employee

• Business Development

  • Oct 2020 - Present

  Bengaluru, Karnataka, India - Marketing of new/existing products to existing/potential customers. - Keeping track of the requirements of various customers, based on end use and affiliated industry. - Managing documentation requirements of customers. - Keeping tabs on the market trends for both imported and domestic products.

• Purchase Assistant

  • Jan 2020 - Oct 2020

  Bengaluru, Karnataka -Responsible for coordinating quotations received from various suppliers and shortlisting based on price and quality parameters. -Coordinating with suppliers for necessary documentation and logistics updates. -Exploring to find alternative sources for raw materials for both domestic and international purchases. - Keeping track of market trends and raw material prices.



Center for Cloud Computing and Big Data, PES University

• India
• Software Development
• 1 - 100 Employee

• Research Intern

  • Jun 2018 - Jan 2020

  Bengaluru Area, India



Akamai Technologies

• United States
• Software Development
• 700 & Above Employee

• Summer Intern

  • Jun 2019 - Aug 2019

  Bengaluru, Karnataka, India



PES University

• India
• Higher Education
• 500 - 600 Employee

• Full Stack Developer for Aatmatrisha 18

  • Feb 2018 - Mar 2018

  Bengaluru, Karnataka, India Tasks handled: Deploying the online merchandise store, Online payment system, Developer page and ensuring responsive design



Crucible of Research and Innovation, PESU

• Intern

  • Jun 2017 - Sep 2017

  Bengaluru Area, India • Worked on Pattern Recognition and Classification. • Research on applying various techniques for classification of websites as phishing, suspicious or legitimate. • Worked on various classification techniques such as K-Nearest Neighbour classifier, Parzen's window algorithm and Logistic Regression. • Came up with a new modified version of Parzen's window algorithm. • Achieved an accuracy of ~95% with Logistic regression, and ~78% with the Modified Parzen's window algorithm.