Senior Lending Credit Risk Model Analyst
RemoteFull-time$100k–$151kPosted Jul 24, 2026
About the Role
H&R Block Financial Services builds credit and banking products that help millions of customers access funds and manage their money. We’re seeking a Senior Lending Credit Risk Model Analyst to support underwriting, portfolio monitoring, and credit strategy across our lending products. Working at the intersection of data science, risk management, and product decisioning, this role will develop analytical and machine-learning solutions that improve credit outcomes while supporting responsible growth. You'll partner closely with Product, Risk, Engineering, Finance, and Operations to solve high-impact business challenges that directly influence customer access to credit and portfolio performance.Day to day, you'll...
- Build and maintain credit risk models, scorecards, and analytical frameworks supporting underwriting, account management, and portfolio monitoring
- Analyze large, complex datasets (application, bureau, tax, and performance data) using Python and SQL to uncover risk insights and portfolio trends
- Develop predictive models, segmentation, and analytics for default risk, customer behavior, and portfolio actions, including experimentation and strategy tests
- Partner with engineering and MLOps teams to productionize models, improve data pipelines, and support ongoing monitoring and performance tracking
- Deliver clear, decision‑ready insights to stakeholders while supporting model governance, documentation, explainability, and continuous data quality improvement
Required Qualifications
- 5+ years of experience in data science, risk analytics, or quantitative analytics with hands‑on model development and deployment
- Strong foundation in predictive modeling and applied statistical methods (e.g., logistic regression, tree‑based models, clustering)
- Experience in consumer credit, fintech, banking, or another regulated financial services environment
- Proficiency in Python and SQL for data analysis, modeling, and automation
- Working knowledge of consumer credit concepts such as underwriting, delinquency, losses, or portfolio performance
- Ability to communicate analytical findings clearly and influence decisions across technical and non‑technical partners
Preferred Qualifications
- Experience with underwriting, account management, collections, fraud, or credit risk strategy
- Familiar with cloud data platforms, modern analytics stacks, or ML deployment processes
- Exposure to model monitoring, validation, documentation, or governance workflows
- Master’s degree in a quantitative discipline