Sr. Analyst, Credit Risk Loss Forecasting

First National Bank•Omaha, NE

10d•$89,828 - $148,215•Hybrid

About The Position

The Senior Analyst is a key technical contributor and analytical expert in FNBO’s credit risk management function, providing advanced analytical support and insights for commercial credit decisions. Within the Commercial Segment, the role supports CECL loss forecasting and stress test forecasting across commercial real estate, commercial and industrial, agriculture, retail consumer, and small business lending portfolios. The work focuses on forecasting, portfolio analysis, and reporting, applying knowledge of modeling methodology and the dual risk rating framework and translating model results into actionable business insight. The Senior Analyst works with minimal supervision, partners closely with the Director and cross-functional stakeholders, and bridges data analysis with practical business application. The position is anticipated to be hybrid, with in-person collaboration three days per week and remote flexibility two days per week. Work location may change based on business needs.

Requirements

Bachelor’s degree in data science, mathematics, statistics, finance, economics, or equivalent experience.
At least 3 years of experience in credit risk forecasting or a related analytical field.
At least 3 years of experience using Python, focused on data analysis and financial modeling.
Strong knowledge of SQL and experience with cloud-based data warehousing solutions such as Snowflake and version control systems such as Git.
Expert knowledge of Excel.
Strong executive presence and ability to present recommendations, influence senior management, and collaborate effectively across teams.
Ability to handle stress, meet deadlines, and work independently.
Strong knowledge of commercial real estate, commercial and industrial, agriculture, retail consumer, and small business lending products and credit risk characteristics.
Ability to independently analyze data, identify complex business trends, and propose solutions.
Proficiency in statistical techniques, modeling, and quantitative analysis.
Knowledge of loss forecasting methods, including vintage analysis, roll-rate, and time series approaches.
Familiarity with AI and machine learning frameworks and their application to credit risk.
Strong executive communication and data storytelling skills.
Technical proficiency in SQL, Python, Excel, and BI tools for data visualization.
Unrestricted work authorization; must not require future sponsorship.

Nice To Haves

Experience with commercial lending products, including commercial real estate, commercial and industrial, agriculture, retail consumer, or small business.
Experience with logistic regression, time series models, and survival analysis techniques.
Familiarity with R in addition to Python and SQL.

Responsibilities

Prepare CECL and stress test forecasts and supporting reports for commercial real estate, commercial and industrial, agriculture, retail consumer, and small business lending portfolios.
Analyze forecast changes and variances against 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 on portfolio performance, model outputs, and forecast accuracy for risk committees, finance, and leadership, including early warning indicators for deteriorating credit quality.
Document model inputs, assumptions, CECL qualitative write-ups, and stress test narratives in accordance with model risk policy, including portfolio-specific risk factors, economic assumptions, and management overlays.
Work with model validators and internal and external auditors, and provide stakeholders with documentation, data, and ad hoc analysis for validation, audit, reserve estimation, portfolio planning, and stress testing requests.
Collaborate with peer credit risk analysts to support ongoing monitoring of the commercial credit risk appetite.
Identify, evaluate, and pilot statistical credit forecasting methodologies and technologies, including AI and machine learning applications, to strengthen analytical capabilities and automate reporting, data preparation, and model execution.