Supply Chain Engineering. We are building on Azure - Big Data, Analytics platform to enable smart business and analytical decision making & BISmartness with Big data and Analytical Smartness with ML,Deep Learning enabled systems. I am part of Supply Chain BI and Analytics team for Microsoft Devices. Handling Supply Chain Global Demand Planning for the BI-Analytics team.

As part of Big Data and Analytics team, I am responsible for handling design and development of end to end ETL life cycle of data and implementing machine learning models based on the solutions that are being handled. CCO Analytics: is about automating the solution(s) that can be used my multiple customers, which is about finding opportunity for the providers in USA health care to make the effective utilization of the resources compared with the benchmark values and finding the readmitting cases using ML techniques. With this I’m able to enable users to save $2M/Year. Data Foundation Team: is responsible for acquiring storing and maintaining the huge amount of health care data from various sources and transforming it into meaning full structured format, so that the data can be a foundation for various solutions developed in the organization.

Solutions Provided: • Warranty Analytics (Manufacturing): Predicting Warranty Cost, Labor Cost, Inventory Stock out using Time series Models and Clustering the Dealers, Warranty Parts, Claim Types etc. • Customer Churn (Telecom): Predicting Likely hood of Customer Churn for a Telco Customer.

Demand Forecasting (Manufacturing): Forecasting the demand for Manufacturing and Production lines. • HR Analytics (IT Services and Operations): Forecasting the Likely hood of an employee resignation and Demand Forecasting for meeting the future demand and Iteration. • Mahindra Agri (Agriculture): Analyzing Agriculture data and providing various Analytics solution for better agriculture procedures. • Elevator Analytics (Elevators): Demand and Complaints Forecasting, and complaints analysis using Complaints Logs. • Insurance and Fraud Analytics (Insurance): Finding the Likelihood of a claim to be a fraudulent. • Health and Life Science (Health Care): Finding the Likelihood of a patient’s readmission (within 30 days) after the treatment. and IEVCS(Intelligent Electrical and Vehicle controlling System), IntelliSearch, etc.

Involved in understanding Healthcare and handling multi source data extraction using Informatica. • Mappings involved transformations like Expression, Sequence Generator, Joiner etc.. • Created sessions for data movement using Workflow Manager • Error checking and testing of the ETL procedures and programs using Informatica session log • Worked extensively on complex mappings using source qualifier, joiner, expressions, aggregators, filters, lookup and update strategy transformations.