Senior Lending Credit Risk Model Analyst

H&R Block•Remote, MO

2d•$100,400 - $150,600•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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