• Led Requirements Gathering through UX Research & customer feedback calls, pinpointing pain points from 9+ customers. This informed the enhancements for 2 Contentstack products: Releases and Workflows

• Strategic Prioritization: Aligned Feature Requirements with business goals, analysis of growth metrics and market potential, driving strategic cohesion

• Competitive Edge through Analysis: Executed Comprehensive Competitve Analysis across 4+ competitors to spotlight high-impact features, enhancing competitiveness

• Collaborated seamlessly with design and engineering teams, conducted thorough market research, engineered a comprehensive product roadmap, crafted precise product requirement documents, analyzed intricate business requirements, and leveraged valuable customer feedback to develop impactful CCaaS features.

• Orchestrated the delivery of personalized solutions for large-scale customers, guiding the product from discovery through User Acceptance Testing (UAT) to a successful product launch, ensuring the realization of desired business outcomes.

• Secured new customers by delivering tailored product demos to prospects, effectively showcasing the product's value proposition and successfully converting them into satisfied customers.

As a part of the RFSW (Radio Frequency Software) - Calibration team, the objective was to create services to automate the calibration of mobile devices and the characterization, that is, measuring and hence automating the computation of the Tx, Rx and FbRx (Feedback loop) powers with different input parameters changing. My task was to create an Antenna Switching Diversity Characterization service, where the Transmission and Receiver powers were to be measured and later computed with respect to switching antennas(the input parameter), which is an important phenomenon to improve radio communication and increase the probability of better and more accurate reception of data packets at the receiver. The task was based on coding in C and C++.

Massive Machine-Type Communications

The objective of this project is to explore the feasibility of multiplexing data using hundreds of antennas to a massive number of IoT devices that are likely to be connected to the network in 5G cellular systems. Massive connectivity deals with a large number of Internet of Things (IoT) devices (generally greater than 1 million) connected to a single cell having a finite number of antennas, of which a maximum of a certain fraction of the total devices are assumed to be active at all times owing to the sporadic nature of traffic in any coherence interval. The main study is to identify the activity of the devices and jointly estimate the channels in use, followed by performance analysis of activity detection and channel estimation by essentially measuring the probability of false alarms and missed detection.