Ayan Mukherjee

I'm

About

Focused and enthusiastic developer with a keen interest in data science and artificial intelligence. By comprehensive exposure to the underlying concepts and applying them vividly to various real world situations, my love for these domains came into being. I am a passionate individual who thrives to build and apply algorithms to solve real-world industry problems.

Birthday: 11 October 2002
Phone: +91 906 840 0339

City: Kolkata, IN
Email: ayan02mukherjee@gmail.com

Interests

Machine Learning

Deep Learning

Computer Vision

Natural Language Processing

Generative AI

Visualization

Algorithms

Image Processing

Education

B.Tech in Computer Science and Engineering

December 2021 - June 2025

Relevant Coursework

Database Management System
Machine Learning
Python Programming Language

Higher Secondary

April 2019 - March 2021

Relevant Coursework

Specialized in PCM

Online Certification

Machine Learning

Advanced Learning Algorithms

Pandas

Deep Learning with Tensorflow

Machine Learning with Python

Neural Networks and Deep Learning

Experience

Cognizant

November 2025 - Present

Programmer Analyst Trainee

Managed end-to-end configuration and execution of incentive compensation processes in SAP Commissions, including creation of compensation plans, elements, and participant assignments for accurate commission calculation.
Executed and monitored commission processing pipelines, ensuring timely and error-free calculation of payouts based on sales performance for multiple payees.
Collaborated with business stakeholders to analyze compensation requirements, implement plan changes, and troubleshoot discrepancies to improve accuracy and efficiency of commission cycles.

Mahindra Accelo Pvt. Ltd.

April 2025 - July 2025

Digital Project Intern

Analysed over 120 hours of production across 4 manufacturing lines (Blanking, Progressive, CTL & Slitting, Transfer) and identified 7 key manual tasks for optimization, leading to an estimated 18% reduction in process delays and saving approximately 22 man-hours per week.
Developed and enforced 3 Power BI-based dashboards to track production efficiency, defect rates, and downtime; enabled data-driven decisions that boosted shift productivity by 12%, reduced rejection rates by 9.5%, and enhanced maintenance scheduling accuracy by 30%.