Dr. Wasif Masood

Leading a team of 9 data scientists and responsible for the end to end delivery of all the data science use cases, starting from idea breif til the daily execution in PROD environment.

As senior manager customer experience analytics, I am responsible for the analytical insights, logical data models, KQI and KPI definition. The responsibility comprises of the E2E Data Analytics including the admission of requirement, consultancy and production startup.

Development and testing of cell balancing module for automobiles. Working as an on-site engineer in Samsung SDI Zettling, Austria. Responsibilities include but not limited to the use of Simulink, TargetLink, dSPACE, Polyspace and SystemDesk-4.2.

Worked as a researcher on projects including Cooperative Relaying in Wireless Networks (RELAY ) and Heterogeneity in Self-Organizing Network Synchronization (HiSONS)

Worked on Connected Cars (CoCar) project, a 3G based research prototype to save automobile accidents and regulate traffic flow. Responsibilities pertained to integration of various components like, Car-Gateway, HomePC, backend network structure, etc. Implementation of Road Hazard Warnings use-case using Bayeux Annotated Services of Jetty Server6 and Ajax based Pull technology.

TRG Tech is a group of companies where my sole responsibilities were related to iSKY. There, I worked on various research studies and customer engagement programs to help companies and brand owners to predict their customers behavior. My major contribution was towards the development of online Reporting Services for Ferrari and the migration of an old portal for the survey of Volkswagen customers from PHP into ASP.NET 2.0.

National Engineers (NE) is a subsidiary of WATEEN Telecom (Pvt.) Limited Pakistan. NE along with its co-partner Sitronics Telecome, developed various OSS/BSS billiing applications for WATEEN Telecome. My major contribution was in the development of the interconnect billing solution for WATEEN.

Network synchronization solutions targeting both centralized and distributed computing are proposed. Solutions are based on dynamic stochastic, Kalman filter and ARIMA models. Tests conducted on various hardware including sensor nodes, WARP boards and Raspberry Pi validate the results.

Machine Learning, Data Science