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Strong Tier-1 brand, mid-level generalist data role, and broad skillset requirements create high applicant competition.
Core analytics skills are transferable, but fraud and credit-card domain knowledge increases industry specificity.
Mandatory 3+ years plus required Python, SAS and SQL skills increase shortlisting strictness to high.
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Develop, implement, and manage fraud prevention strategies across the credit card fraud lifecycle to reduce losses and optimize customer experience.
Perform advanced analytics and interpret complex data to identify fraud risk trends, key risk indicators, and opportunities for process and decisioning optimization.
Collaborate with cross-functional teams and present data-driven insights and recommendations to managers and executives, utilizing modern tools like large language models for scalable improvements.
Bachelor's degree in engineering, statistics, mathematics, or another quantitative field, or 3+ years of risk management or quantitative experience.
Proficiency in Python, SAS, and SQL with ability to query large datasets and translate results into actionable business recommendations.
Experience delivering analytical recommendations to leadership.
Work Experience Required: At least 3 years in risk management or other quantitative experience, or equivalent education.
Strong quantitative background with experience in fraud risk analysis and strategy within payments or credit card domains.
Experience working in cross-functional teams and communicating effectively with senior leadership on complex analytical topics.
Familiarity with advanced analytics tools and emerging technologies such as machine learning, large language models, AWS, and Snowflake is preferred.