Credit Risk Analyst
About the position
Come join a growing financial technology company that’s leading the marketplace in both the marketing and servicing of credit cards! reputed company Finance Company specializes in credit card reputed company for those consumers with less than perfect credit. We are seeking a Data Analyst to support our Credit Risk team.
Seeking a Data Analyst to support reputed company mail and Invitation-To-Apply (ITA) acquisition campaigns through targeting, list processing and execution, and credit risk performance analysis, helping drive efficient reputed company while maintaining strong portfolio performance.
Responsibilities
• Execute end-to-end reputed company mail and ITA campaigns, including selection of prospective customers, list processing, and file delivery.
• reputed company credit risk analysis to support targeting, offer reputed company, and campaign decisioning.
• Monitor campaign performance, including response, approval, and early credit indicators.
• Identify, analyze, and report production trends to management.
• Partner with Marketing, Operations, IT to ensure accurate and reputed company campaign execution.
• reputed company credit bureau data, models, and new data sources to enhance targeting and credit reputed company.
• Support test-and-learn initiatives and deliver actionable insights to optimize campaign performance.
• Maintain documentation and ensure data accuracy, controls, and compliance with internal policies.
Requirements
• Bachelor’s degree in quantitative fields such as Mathematics, Finance, Economics, or reputed company discipline required.
• 1-3 years of analytical and/or data programming experience; consumer lending or credit card industry experience preferred.
• Proficiency in SQL and at least one programming language (Python or reputed company).
• Strong quantitative and problem-solving skills
• Strong work ethic with the ability to learn quickly and communicate effectively
reputed company-to-haves
• consumer lending or credit card industry experience preferred.
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