Sourabh Chandel

Led a cross-functional team in developing and optimizing LLMs and GenAI solutions, improving operational efficiency by 75%. Managed team collaboration and guided technical strategies to ensure seamless execution of complex projects. Developed patient switch prediction models with 85% accuracy and 0.91 AUC, and Survival Analysis prediction models using LAAD data.

Developed and deployed ML-based Credit Risk Propensity and Fraud Detection models, improving risk prediction accuracy by 20% and increasing approved loan volumes by $5M. Created a Scenario Simulation Platform for Healthcare & Life Sciences, optimizing distribution strategies and increasing revenue by $13.3M while reducing costs by 15%. Led cross-functional teams to implement machine learning model

Optimized marketing spending for healthcare brand campaigns through Market Mix Modelling, leading to a 20% increase in ROI. Designed and developed predictive models to drive business outcomes, collaborating with clients to create tailored solutions and improving model performance by 18% through feature engineering and hyperparameter tuning.

Analyzed customer behavior and financial needs to develop personalized products, risk models, fraud detection systems, and predictive pricing, enhancing AML compliance through data insights. Reduced Non-Performing Assets (NPAs) from 13% to 8% by developing ML solutions to identify high-risk cohorts. Implemented a retention model for fraud detection, improving early detection by reducing false posi

Delivered advanced analytical models, predictive algorithms, and AI-powered tools to extract actionable insights, driving US Commercial strategies and ensuring alignment with strategic objectives. Reduced false positives by 92% and improved diagnostic accuracy for oncology care, achieving an F1 score of 0.88 using Random Forest and Gradient Boosting. Improved screening and care accuracy by 90% acr

Increased targeted campaign effectiveness by 30% and reduced churn by 18% by leveraging K-means & hierarchical clustering for customer segmentation. Developed Boosting & Random Forest for predictive models, optimizing loyalty programs and mitigating churn risk.

Contributed to a 15% reduction in default rates and increased loan approval volumes by generating analytical insights and reports focused on lending analytics.

Pursued a Master of Business Administration with a specialization in Finance. Gained expertise in financial management, investment strategies, and corporate finance principles.

Completed a Bachelor of Engineering degree focusing on Mechanical Engineering. Developed a strong foundation in mechanical design, thermodynamics, and manufacturing processes.