Credit Strategy Analyst

Job Summary

*Hybrid-Must work a percentage of time each week in the Omaha, NE office.

We are seeking a Credit Strategy Analyst who will be responsible for designing, implementing, and continuously improving credit decision strategies and analytical frameworks that support consistent, efficient, and scalable credit practices across the Associations. As an analyst, you will leverage advanced analytics, modeling, machine learning (ML), and business insights to assess credit and portfolio risks, optimize decision quality, inform sound underwriting outcomes, and ensure compliance with governance and regulatory standards.   

Essential Duties & Responsibilities

Design, implement, and continuously improve credit decision strategies and models that drive consistent underwriting outcomes and scalable credit practices. 

• Develop and maintain portfolio analytics and monitoring across loan application, origination, renewal, and underwriting processes. 
• Evaluate credit policy outcomes, decision strategies, and portfolio performance to identify opportunities for improvement and inform leadership decisions. 
• Partner with cross-functional teams to deliver analytical solutions that integrate into automated decisioning, monitoring, and reporting workflows. 
• Support model governance activities including documentation, validation support, and alignment with regulatory and internal governance expectations. 
• Translate complex analytical findings into clear, actionable insights for management committees, leadership, and business partners. 
• Contribute to the evolution of automated credit decisioning and monitoring practices, ensuring transparency, accuracy, and alignment with organizational objectives. 

Education Requirements

Years of Experience

• 2+ years of experience in credit, finance, or risk management. 
• Strong collaboration, communication, and presentation skills with the ability to interact with teams at every level of the organization. 
• Creativity to develop data visualizations that can be easily interpreted by stakeholders. 
• Demonstrated ability to break down technical concepts for non-technical audiences. 
• Strong proficiency with SQL, Alteryx, and Excel for data extraction, analysis, and automation (e.g., complex joins/CTEs, Power Query/Power Pivot, SSAS cubes, advanced formulas). 
• Working knowledge or willingness to develop knowledge of statistical programming languages (e.g., R, Python). 
• Experience with data visualization and dashboard applications (e.g., Power BI). 
• Familiarity with cloud data platforms (e.g., Snowflake) preferred. 
• Knowledge of model risk management frameworks and documentation practices preferred. 
• Understanding of statistical modeling techniques and measures (e.g., logistic regression, decision trees/GBM, scorecards, segmentation, drift/stability analysis, ROC, KS) and their application in credit decisioning. Direct experience preferred; demonstrated aptitude and willingness to learn will also be considered.