Chief Data Scientist - Credit Risk & ML Strategy

FNBO; South Dakota; Hybrid; USD 159,000 - 270,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401k with matching

Time Off programs

HSA/Dependent Care

Job summary

FNBO is seeking a Sr. Director, Advanced Data Scientist to lead the data science and modeling function within the Partnership Division.

You will define multi-year modeling roadmaps for credit risk and portfolio management and oversee development of advanced ML models for credit decisioning and risk management. You will oversee model development, validation alignment, and reporting to senior leadership, while advancing explainability, fair lending, and regulatory expectations.

Qualifications

• This role requires advanced degrees in quantitative fields and PhD strong preference for research excellence.
• 7+ years of experience building statistical or ML models in financial services or credit context.
• Strong coding in Python and/or R with SQL proficiency.
• Experience with gradient boosting, neural nets, and explainability tools.

Responsibilities

• Define multi-year modeling roadmaps for credit risk and portfolio models.
• Lead end-to-end model development from problem to production deployment.
• Communicate strategy and results to senior leaders with executive-ready reporting.
• Advance fair lending analyses and interpretable ML methods.
• Monitor model performance and provide recalibration recommendations.

Skills

Python

R

SQL

XGBoost/LightGBM

Neural networks

SHAP

Partial dependence

Education

Master's degree

PhD

Tools

XGBoost

LightGBM

Job description

FNBO is seeking a Sr. Director, Advanced Data Scientist to lead the data science and modeling function within the Partnership Division.

You will define multi-year modeling roadmaps for credit risk and portfolio management and oversee development of advanced ML models for credit decisioning and risk management. You will oversee model development, validation alignment, and reporting to senior leadership, while advancing explainability, fair lending, and regulatory expectations.