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Citi - Fraud Prevention - Business Analytics Sr Analyst - Hybrid

Job Description

As part of the Fraud Analytics, Modeling & Intelligence organization, this role executes the fraud analytics and strategies supporting Citi’s North American and global credit card and retail bank businesses. This includes leveraging data to identify fraud trends, designing and implementing strategies to prevent and mitigate fraud attacks across the fraud lifecycle, including application and synthetic ID fraud, account takeover and sophisticated new attack schemes.
This role partners closely with Fraud Policy, Operations and various partners to keep apprised of business and technology direction in order to determine potential and existing fraud impacts.


  • Leverage data and advanced analytics to derive patterns, trends and insights, and perform risk/reward trade-off analysis.
  • Ownership and management of fraud rules, scores, and detection strategies, Risk appetite execution, POS interdiction strategies and defect analysis.
  • Collaborate with cross-functional teams to provide strategy recommendations based on data and trend analysis, and implement mitigation strategies.
  • Build effective relationships within and outside the Fraud organization to help ensure successful and timely execution of key portfolio priorities.
  • Leverage knowledge of information acquired to identify potential process gaps and opportunities for improving effectiveness of controls and governance processes.
  • Generate and manage regular and ad-hoc reporting to enable effective monitoring and identification of emerging trends.


  • Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline.
  • 3+ years in relevant field.
  • 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 Tableau
  • Excellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade-off analysis.
  • Good written and verbal communication skills, with ability to connect analytics to business impacts; comfortable presenting to peers and management.
  • Extremely detail-oriented; intellectual curiosity
  • Ability to multi-task and work against tight deadlines.
  • Ability to work independently with baseline instructions/guidelines from management

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.