Manager - Business Analysis - Business Analyst

Develop strategies, manage resources, lead team meetings, and monitor performance metrics.

• 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.

• 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.