Data Scientist - Merchant and Loyalty Rewards Protection Analyst, AVP

Data Scientist - Merchant and Loyalty Rewards Protection Analyst, AVPThis role will advance detection and risk modeling strategies across merchant integrity and rewards ecosystems. This role will develop scalable behavioral models and detection frameworks that proactively identify merchant impersonation, inauthentic business activity, and loyalty/rewards exploitation.This individual will translate emerging risk patterns into measurable signals and deploy analytic solutions that enhance enterprise protection controls. This role will manage and execute fraud analytics and fraud detection strategies supporting Citi's North American and global credit card and retail bank businesses.Responsibilities:Design, develop, and optimize detection models addressing merchant impersonation, inauthentic entities, synthetic or coordinated activity, loyalty/rewards accrual and redemption anomalies, and promotion exploitation.Develop and refined both micro-level and macro-level portfolio-wide anomaly detection, linkages, and behavioral anomalies.Apply graph/network analytics to identify collusion or coordinated abuse patternsOwn and optimize fraud rules, risk scores, and model thresholds.Monitor performance and iterate based on emerging patternsTranslate intelligence into scalable detection logic.Provide quantitative recommendations to enhance controls and reduce exposure.Partner with Fraud Strategy, Fraud Policy, Intelligence, and Product Teams.Prioritize and provide a clear line of sight to the most critical work to partners and team members.Mentor and coach junior team members.Qualifications :Bachelor's Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline.3+ years experience in analytics and modeling or relevant areaExtensive experience working with:Big Data environment with hands on coding experience within various traditional (SAS, SQL, etc.) and/or open source (i.e. Python, Impala, Hive, etc.) tools.Traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.Data visualization tools, such as TableauExcellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade-off analysisAbility to build effective presentations to communicate analytical findings to a wide array of audiences.Effective cross-functional project, resource, and stakeholder engagement and management, with ability to effectively drive collaboration across teams.Ability to make decisions independently with minimal guidance from management.