Assistant Manager - Model Validation - Fraud Analytics

As a Fraud Analytics professional, you will be responsible for the end-to-end development, validation, and performance tracking of fraud risk models. You will use tools such as Python, PySpark, and SQL to monitor fraud risk models in production, analyze data drift, and optimize model performance. Key Responsibilities: - Validate fraud risk models in the production environment - Track model performance metrics such as AUC, KS, Precision, Recall, FPR, and Capture Rate - Conduct data drift analysis using PSI/CSI - Identify concept drift and recommend retraining triggers - Perform threshold analysis and assess strategy impact - Extract and analyze large transactional datasets using SQL and PySpark - Build monitoring dashboards and monthly performance reports - Collaborate with cross-functional teams including Fraud Strategy, Risk, and Model Development - Support audit and model validation documentation Required Skills: Technical Skills: - Strong SQL skills including complex joins, aggregations, and performance optimization - Proficiency in Python with knowledge of Pandas, NumPy, and Sklearn model performance evaluation - Experience with PySpark for large-scale data handling - Familiarity with calculating drift metrics such as PSI and CSI - Understanding of fraud KPIs and alert-based models - Experience working with imbalanced datasets Domain Skills: - Understanding of fraud lifecycle including transaction fraud, card fraud, and digital fraud - Knowledge of model monitoring frameworks - Exposure to model governance or validation processes is preferred Good to Have: - Experience working with US/UK banking clients - Exposure to real-time fraud models - Understanding of regulatory expectations in model risk management - Experience with dashboarding tools such as Power BI or Tableau (Note: No additional details about the company were provided in the job description.) As a Fraud Analytics professional, you will be responsible for the end-to-end development, validation, and performance tracking of fraud risk models. You will use tools such as Python, PySpark, and SQL to monitor fraud risk models in production, analyze data drift, and optimize model performance. Key Responsibilities: - Validate fraud risk models in the production environment - Track model performance metrics such as AUC, KS, Precision, Recall, FPR, and Capture Rate - Conduct data drift analysis using PSI/CSI - Identify concept drift and recommend retraining triggers - Perform threshold analysis and assess strategy impact - Extract and analyze large transactional datasets using SQL and PySpark - Build monitoring dashboards and monthly performance reports - Collaborate with cross-functional teams including Fraud Strategy, Risk, and Model Development - Support audit and model validation documentation Required Skills: Technical Skills: - Strong SQL skills including complex joins, aggregations, and performance optimization - Proficiency in Python with knowledge of Pandas, NumPy, and Sklearn model performance evaluation - Experience with PySpark for large-scale data handling - Familiarity with calculating drift metrics such as PSI and CSI - Understanding of fraud KPIs and alert-based models - Experience working with imbalanced datasets Domain Skills: - Understanding of fraud lifecycle including transaction fraud, card fraud, and digital fraud - Knowledge of model monitoring frameworks - Exposure to model governance or validation processes is preferred Good to Have: - Experience working with US/UK banking clients - Exposure to real-time fraud models - Understanding of regulatory expectations in model risk management - Experience with dashboarding tools such as Power BI or Tableau (Note: No additional details about the company were provided in the job description.)