Sr. Analyst, Credit Risk Loss Forecasting at First National Of Nebraska in Omaha

Job description

At FNBO, our employees are the heart of our story—and we’re committed to their success! Please see below the details of this career opportunity and how it fits into our organization’s success. Summary of the Job: The Senior Analyst serves as a key technical contributor and analytical expert within the bank's credit risk management function, providing advanced analytical support and insights to drive informed credit decisions on commercial portfolios. Within the Commercial Segment, this role supports the team's CECL loss forecasting and stress test forecasting efforts across commercial real estate, commercial & industrial, agriculture, retail consumer, and small business lending portfolios. This role focuses on CECL and stress test forecasting, portfolio analysis, and reporting, with knowledge of modeling methodology and the dual risk rating framework and the ability to articulate model results into actionable business insight. The Senior Analyst works with minimal supervision, partners closely with the Director and cross-functional stakeholders and serves as a bridge between data analysis and practical business application. About This Role: Major Responsibilities: Prepare CECL and stress test forecasts and supporting reporting materials across commercial real estate, commercial & industrial, agriculture, retail consumer, and small business lending portfolios. Conduct deep-dive analysis on forecast changes and variances relative to portfolio trends and macroeconomic variables, and present findings, key drivers, and business implications to senior management and risk committees. Prepare ongoing monitoring reports and dashboards tracking portfolio performance, model outputs, and forecast accuracy for risk committees, finance, and leadership, including early warning indicators for detecting deteriorating credit quality across portfolios and segments. Document model inputs, assumptions, and CECL qualitative write-ups / stress test narratives in accordance with model risk policy, capturing portfolio-specific risk factors, economic assumptions, and management overlays. Work with model validators and internal/external auditors, and partner with stakeholders, providing documentation, data, and ad hoc analysis supporting validation, audit, reserve estimation, portfolio planning, and stress testing scenario requests. Collaborate with peer credit risk analysts to support ongoing monitoring of the commercial credit risk appetite. Identify, evaluate, and pilot new statistical credit forecasting methodologies and technologies including AI and machine learning applications to strengthen the team's analytical toolkit and drive process automation in reporting, data preparation, and model execution. The Ideal Candidate for This Role: Education and Experience: Bachelor's degree in data science, mathematics, statistics, finance, economics, or equivalent experience. 3+ years of experience in credit risk forecasting or a related analytical field, preferably with exposure to commercial lending products (CRE, C&I, agriculture, retail consumer, or small business). 3+ years of experience using Python, with a focus on data analysis and financial modeling. Strong knowledge of SQL and experience working with cloud-based data warehousing solutions (e.g., Snowflake) and version control systems (e.g., Git). Expert knowledge of Excel. Strong executive presence, with the ability to present recommendations, influence senior management, and collaborate effectively within cross-functional teams. Demonstrated ability to handle stress, meet deadlines, and work independently. Experience with logistic regression, time series models, and survival analysis techniques, and familiarity with R (in addition to Python and SQL), is a plus. Knowledge & Skills : Strong knowledge of commercial real estate, commercial & industrial, agriculture, retail consumer, and small business lending products and credit risk characteristics. Ability to work independently with data to identify