Business Analytics Lead Analyst (Digital, Credit Card, First Party Fraud) Hybrid, VP

$117,440 - $176,160/Yr

Citigroup - Erlanger, KY

Full-time - Mid Level

Erlanger, KY

Credit Intermediation and Related Activities

About the position

This role within the Fraud Analytics, Modeling & Intelligence organization at Citi focuses on managing and executing fraud analytics and strategies for the North American and global credit card and retail bank businesses. The position involves leveraging data to identify fraud trends and designing strategies to prevent and mitigate various types of fraud, including application fraud and account takeover. The role requires close collaboration with Fraud Policy, Operations, and other partners to assess fraud impacts and implement effective solutions.

Responsibilities

• Leverage analytics to identify enhancement opportunities and insights while ensuring adherence to Fraud Policy.
• Manage fraud rules, scores, detection strategies, and risk appetite execution.
• Collaborate with cross-functional teams to provide strategy recommendations based on data and trend analysis.
• Build relationships within and outside the Fraud organization for successful execution of portfolio priorities.
• Generate and manage regular and ad-hoc reporting to monitor and identify emerging trends.
• Assess manual and automated processes to identify potential gaps and opportunities.
• Lead analytical projects within the retail bank fraud and digital fraud analytics team.
• Utilize advanced predictive analytical techniques to support Retail Bank lines of business.
• Build risk segmentation/mitigation strategies using customer data.
• Mentor and coach junior team members.

Requirements

• Bachelor's Degree in statistics, mathematics, physics, economics, or other analytical or quantitative discipline.
• 5+ years of experience in analytics and modeling or relevant area.
• Extensive experience with Big Data environments and hands-on coding in traditional (SAS, SQL) and/or open source (Python, Impala, Hive) tools.
• Experience with traditional and advanced machine learning techniques and algorithms (e.g., Logistic Regression, Gradient Boosting, Random Forests).
• Proficiency in data visualization tools such as Tableau.
• Excellent quantitative and analytic skills to derive patterns and insights.
• Ability to build effective presentations to communicate findings to diverse audiences.
• Effective cross-functional project and stakeholder management skills.
• Ability to make independent decisions with minimal guidance.

Benefits

• Medical, dental & vision coverage
• 401(k)
• Life, accident, and disability insurance
• Wellness programs
• Paid time off packages including vacation, sick leave, and paid holidays