Partha Majumdar

Partha started his career in 1989 as a programmer. In his first assignment, he was involved in development of a Cricket Tournament management system as a part of the team from Centre for Development of Telematics (C-DOT) requested by the Prime Minister of India, Mr. Rajiv Gandhi. Since then Partha has developed Tea Garden automation solution, Hospital Management solution, Travel Management solution, Manufacturing Resource Planning (MRP II) solution, Insurance Management solution and Tax automation solution (for Government of Thailand).

Partha got involved in Telecom solution with project from Total Access Communications, Bangkok in 1996. Partha developed the completed solution architecture and designed & developed the complete infrastructure services and primitives on top of which the end-to-end Customer Care and Billing solution was developed between 1996-1998.

Partha has worked for companies including Amdocs, Portal, Siemens and has developed key components of their solutions. For Siemens, Partha developed the complete BSS suite.

Partha worked with Mobily, Saudi Arabia as the Enterprise Architect and has first-hand of experience of work inside a Telecom Operator.

Partha started his own company, Majumdar Consultancy Pvt Ltd, in 2014. He partnered with a Dubai based businessman to open SI Solutions India Pvt Ltd in 2016. In 2019, he joined Tools and Solutions, Saudi Arabia as Director - Professional Services to establish the Professional Services business.

Partha has recently developed a Remote Control, which can be controlled from a Web Site. The Remote Control can in turn control any device. The Remote Control to be controlled needs having Infra-Red sensing capabilities. The Remote Control is controlled through DragonBoard 410C through a Android Program.

Partha has been working on fine tuning the algorithm for a Access Control System through Face Recognition. The program has been developed using Convolutional Neural Network (CNN).

Partha has also developed a software which tries to predict the Stock Market. The solution has been developed using Recurrent Neural Network (RNN). The solution presently predict with an accuracy of 77%.