Citi Group Business Analytics Lead Analyst 

United States, Texas, San Antonio 

366255142

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.
• Lead key analytical projects within the retail bank fraud and digital fraud analytics team and support the Retail Bank lines of business by utilizing advanced predictive analytical and statistical techniques.
• Leverage customer data to build risk segmentation/ mitigation strategies and complete complex analyses to identify authentication strategy and procedure gaps, manage implementation process across several systems to affect change.
• 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:

• Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline.
• 5+ years experience in analytics and modeling or relevant area.
• Extensive 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.
• 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.