• Worked in the personalization team of a leading European retailer to deliver personalized offers based on customer behavior and demographics. resulting in enhanced customer engagement.• Used churn prediction model to classify customers into risk groups. Targeted engagement strategies were then designed based on these risk levels to minimize churn and optimize customer retention and engagement.• Developed a robust customer segmentation model using Gaussian Mixture Models (GMMs)… Show more • Worked in the personalization team of a leading European retailer to deliver personalized offers based on customer behavior and demographics. resulting in enhanced customer engagement.• Used churn prediction model to classify customers into risk groups. Targeted engagement strategies were then designed based on these risk levels to minimize churn and optimize customer retention and engagement.• Developed a robust customer segmentation model using Gaussian Mixture Models (GMMs) along with PCA for variable reduction. Utilized insights from customer profiling to understand purchase preferences, enabling the development of effective strategies for targeted marketing campaigns.• Measured campaign performance through A/B testing, uplift calculation, and comparison of metrics between exposed and control groups. Used KMeans clustering and stratified sampling for control group selection, followed by statistical tests to validate the resampling. Show less

• Developed Credit Risk Scoring model for a BFSI client using ensemble classification methods. Objective was to assess customer creditworthiness, identify key risk factors, and mitigate default risk to minimize financial losses for lenders.• Built a term deposit propensity model for a banking institution using XGBoost and SVM. Aim was to project the likelihood of potential customers subscribing to term deposits.