🌟 Professional Overview: With 14 years of total professional experience and 10 years specializing in Data Science, Credit Risk, and Customer Analytics, I bring a wealth of expertise to the table. My focus areas include developing and implementing various analytics scorecards,models and dashboards in the banking sector. 📈 Scorecards & Analytics Models: Credit Risk Models: PD, LGD, EAD, IFRS9, EL Analytics. Customer Analytics Models: Loyalty, Churn, NetLift. Scorecards: Application, Behavioural, Collection. 🔍 Technical Proficiency: Statistical Modeling & Machine Learning: Regression, Classification, Cluster Analysis. Predictive Models: Logistic Regression, Linear Regression, KNN, Naive Bayes, SVM, Decision Trees, Random Forest, Xgboost. Clustering & Feature Reduction: PCA, t-SNE, K-Means. Programming & Tools: SAS Enterprise Miner, SAS Enterprise Guide, Advanced Excel, Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn), SQL. Natural Language Processing (NLP) Dashbording tools: Tableau, Power BI, Micro-Strategy. 💡 Strategic Insight: My approach is rooted in a strong orientation towards statistical modeling and machine learning, underpinned by deep domain knowledge in Credit Risk and Customer Analytics. I'm committed to driving business insights and impactful solutions in the banking industry.