Business Analytics Sr. Manager

As part of the Fraud Analytics, Modeling & Intelligence organization, this role manages and 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. This role also may also have direct or indirect management responsibility of team members. Responsibilities: Leverage analytics to identify enhancement opportunities and more granular insights that can be acted upon, while ensuring adherence to Fraud Policy. 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. Generate and manage regular and ad-hoc reporting to enable effective monitoring and identification of emerging trends. Continually assess manual and automated processes to identify potential process gaps and opportunities. Build effective relationships within and outside the Fraud organization to help ensure successful and timely execution of key portfolio priorities. 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: 5+ years experience in analytics and modeling or relevant area. 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. Ability 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. Education: Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline. Master's Degree or PhD preferred. This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.