Yash D Jaiswal

Led POC development to predict customer churn using a limited dataset. Conducted exploratory data analysis (EDA) to identify anomalies, extract insights, and address business queries to support decision-making. Developing ML models to score prospect accounts based on likelihood to convert, recommending tailored solutions to prioritize leads and enhance sales/revenue.

Built risk models to predict probability of longer-term default using open banking data. Customer segmentation using clustering to identify patterns in customer profiles and behaviour. Worked on optimizing the existing model to reduce the default rate, optimizing credit underwriting cost and credit underwriting risk. Gaining insights from data on several aspects such as customer return rate, moratorium, delinquency rates, active customers, etc. Developed ML models to improve the conversion rate.

Fault Detection in LRU: Working on developing analytics for detecting anomaly in engine components (mainly LRU: Line Replaceable Units) for business jet engines. Client: Rolls-Royce (R Data Labs). Role & Responsibility: Understanding functioning of various components, development of analytical models (using Python) for detecting anomaly using engine flight test data. Understanding requirements and developing suitable analytics for detecting various anomalies such as gradual deterioration of component, abnormal engine start, etc.

Was part of prestigious EEDP (Edison Engineering Development Program) training which gave exposure to various domains in Engineering (Mechanical Design, Analysis and Analytics).