Sanjeet Mukherjee
About Sanjeet Mukherjee
Sanjeet Mukherjee is a Senior Software Engineer currently working at Quantcast in London. He has a background in software engineering with previous roles at Deutsche Bank, Kobalt, and Ocado Technology, and holds a Master of Engineering in Computer Science from the University of Southampton.
Work at Quantcast
Sanjeet Mukherjee has been employed at Quantcast as a Senior Software Engineer since 2020. In this role, he serves as the tech lead on the control platform team, where he is responsible for monitoring and controlling significant financial expenditures across a globally distributed real-time bidding (RTB) platform. His contributions include designing and implementing a feature-set for dynamic supply pricing, which has resulted in several million USD in new client billings.
Previous Experience at Deutsche Bank
Before joining Quantcast, Sanjeet Mukherjee worked at Deutsche Bank as a Software Engineer from 2017 to 2019 in London. During his tenure, he contributed to various software engineering projects. He also completed a Software Engineering Internship at Deutsche Bank in 2016, where he gained on-site experience for two months.
Experience at Kobalt and Ocado Technology
Sanjeet Mukherjee worked at Kobalt as a Software Engineer for one year, from 2019 to 2020, in London. Prior to that, he was a Software Development Intern at Ocado Technology for two months in 2015, where he began his career in software development.
Education and Expertise
Sanjeet Mukherjee earned a Master of Engineering (MEng) degree in Computer Science from the University of Southampton, where he studied from 2013 to 2017. During his time at the university, he also served as a Student Ambassador from 2014 to 2017, engaging with prospective students and representing the institution.
Technical Contributions and Achievements
Sanjeet Mukherjee has made significant technical contributions throughout his career. He has maintained pixel and bidding systems capable of handling over 4 million requests per second. Additionally, he consolidated more than 25 methods of loading machine learning models onto a unified platform, which eliminated fortnightly modeling incidents. His redesign of the spend feedback system has successfully reduced global spend discrepancies to zero, recovering multiple million USD in lost billings annually.