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Key Responsibilities

• Collect, clean, and analyze large, complex datasets from multiple sources. ·Develop predictive models and machine learning algorithms to support decision-making and improve business performance.
• Translate business problems into data-driven solutions with measurable impact.
• Develop and deploy machine learning models to detect, predict, and prevent fraudulent transactions and behavior patterns.
• Analyze large volumes of structured and unstructured data from multiple sources to identify fraud trends and root causes.
• Collaborate with fraud operations, engineering, and compliance teams to implement real-time fraud detection solutions.
• Design and monitor KPIs to evaluate model performance and improve fraud detection systems over time.
• Conduct deep-dive investigations into fraud cases, creating detailed reports and actionable insights.
• Stay current with emerging fraud techniques, industry best practices, and data science tools.

Required Qualifications

• Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field.
• 10+ years of professional experience in data science
• Proficient in Python, SQL, SAS and machine learning techniques
• Experience in responsible use of AI if used in solution design

·Strong analytical skills and the ability to identify patterns and trends from data

• Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure).
• Strong understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis.
• Experience with visualization tools like Tableau and Power BI.