Manager-Fraud Analytics

Assistant Manager Fraud Analytics | Data Science & Analytics | Hybrid | Banking & Financial Services Role Summary An opportunity for a Data Analytics professional to join the Fraud Analytics function within a global banking and financial services environment. The role focuses on leveraging data science, machine learning, and advanced analytics to detect fraud patterns, enhance risk controls, and support data-driven decisionmaking. The position involves working with largescale datasets, predictive modeling, and crossfunctional collaboration in a fastpaced, regulated environment. Location: Hybrid (2 days office, 3 days work from home) Shift Timing: 11:00 AM 8:00 PM Key Responsibilities Data Collection & Processing Extract, collect, and analyze data from multiple internal and external sources Perform data cleaning, transformation, and validation for analytical use Ensure data quality, lineage, governance, and control compliance Design and maintain automated data pipelines for efficient data processing Improve data workflows and support automation initiatives Develop and deploy statistical and machine learning models for fraud detection Identify fraud patterns, anomalies, and emerging risk trends Build predictive models to forecast fraud risk and business outcomes Reporting & Insights Develop dashboards, reports, and analytical insights for stakeholders Translate complex data findings into actionable business recommendations Stakeholder Collaboration Work closely with risk, business, and operations teams Present analytical insights to both technical and nontechnical stakeholders Support implementation of fraud prevention policies and controls Enhance analytical frameworks, reporting systems, and automation processes Identify opportunities where data science can improve risk management and efficiency Required Skills & Experience Experience in Fraud Analytics, Risk Analytics, Data Science, or Data Analytics within Banking/Financial Services Strong proficiency in SQL and SAS Experience working with large datasets and analytical workflows Strong knowledge of statistical modeling and predictive analytics Understanding of data governance, data quality, and control frameworks Strong communication and stakeholder management skills Ability to work independently in a dynamic environment Preferred Skills Advanced SQL and SAS programming expertise Machine Learning and predictive modeling experience Exposure to fraud detection or financial crime analytics Experience in building data pipelines and automation frameworks Knowledge of data visualization tools Strong business acumen and problemsolving skills Assistant Manager Fraud Analytics | Data Science & Analytics | Hybrid | Banking & Financial Services Role Summary An opportunity for a Data Analytics professional to join the Fraud Analytics function within a global banking and financial services environment. The role focuses on leveraging data science, machine learning, and advanced analytics to detect fraud patterns, enhance risk controls, and support data-driven decisionmaking. The position involves working with largescale datasets, predictive modeling, and crossfunctional collaboration in a fastpaced, regulated environment. Location: Hybrid (2 days office, 3 days work from home) Shift Timing: 11:00 AM 8:00 PM Key Responsibilities Data Collection & Processing Extract, collect, and analyze data from multiple internal and external sources Perform data cleaning, transformation, and validation for analytical use Ensure data quality, lineage, governance, and control compliance Design and maintain automated data pipelines for efficient data processing Improve data workflows and support automation initiatives Develop and deploy statistical and machine learning models for fraud detection Identify fraud patterns, anomalies, and emerging risk trends Build predictive models to forecast fraud risk and business outcomes Reporting & Insights Develop dashboards, reports, and analytical insights for stakeholders Translate complex data findings into actionable business recommendations Stakeholder Collaboration Work closely with risk, business, and operations teams Present analytical insights to both technical and nontechnical stakeholders Support implementation of fraud prevention policies and controls Enhance analytical frameworks, reporting systems, and automation processes Identify opportunities where data science can improve risk management and efficiency Required Skills & Experience Experience in Fraud Analytics, Risk Analytics, Data Science, or Data Analytics within Banking/Financial Services Strong proficiency in SQL and SAS Experience working with large datasets and analytical workflows Strong knowledge of statistical modeling and predictive analytics Understanding of data governance, data quality, and control frameworks Strong communication and stakeholder management skills Ability to work independently in a dynamic environment Preferre