About this role
Develop strategies, manage resources, lead team meetings, and monitor performance metrics.
• Build predictive models for real-time fraud detection systems. • Evaluate and optimize existing ML models for performance, scalability, and explainability. • Apply deep learning and advanced analytics for behavior analysis and risk profiling. • Analyze transaction data to identify patterns, anomalies, and fraud trends. • Perform exploratory data analysis (EDA) to understand customer behavior and potential fraud vulnerabilities. • Translate data-driven insights into actionable recommendations for leadership and stakeholders. • Collaborate with fraud strategy and operations teams to enhance fraud prevention frameworks. • Validate and monitor predictive models for real-time fraud detection systems using valid monitoring metrics/KPI’s. • Prepare technical documents related to fraud models and model validation. Validate the models using various techniques and KPIs, out of time validation etc. • Set up monthly/quarterly and annual monitoring for the models using valid monitoring metrics/KPI’s. • Perform Root Cause Analysis in case of deterioration of model performance/data issues. • Evaluate and optimize existing ML models for performance, scalability, and explainability. • Apply deep learning and advanced analytics for behavior analysis and risk profiling as part of • Analyze transaction data to identify patterns, anomalies, and fraud trends. • Perform exploratory data analysis (EDA) to understand customer behavior and potential fraud. • Translate data-driven insights into actionable recommendations for leadership and stakeholders. • Collaborate with other teams like fraud strategy to enhance fraud prevention frameworks.
• 3-5+ years of experience in analytics preferably in Banking and Financial Services • A minimum of 3 years of hands-on experience working on monitoring and validation of Machine Learning models to solve analytical use cases. • Solid understanding of banking products, fraud types (e.g., account takeover, synthetic fraud, identity theft), and transaction systems. • Knowledge of various statistical techniques used in analytics (regression, ML Models, Monitoring Metrics like KS, PSI, CSI, MAPE, Confusion Metrics etc.) • Excellent problem-solving and analytical skills, with the ability to work on complex projects and deliver high-quality results. • Proficiency in programming languages such as Python, and experience with ML related libraries. • Experience with large-scale data processing and distributed computing frameworks is a plus. • Strong communication skills, both written and verbal, with the ability to convey complex ideas to diverse stakeholders.