Sr. Analyst, Credit Risk Loss Forecasting

• →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.

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 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.

This role may have a specific starting pay within this range.

Final compensation offer to candidate may vary from posted hiring range based upon work experience, education, and/or skill level.

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