Credit Data Modeler - Remote

UnitedHealth Group•Salt Lake City, UT

1d•Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We’re seeking a sharp, experienced Credit Modeler to build and scale the credit modeling infrastructure behind our Consumer and Small Business lending products. In this hands-on role, you will develop and maintain new and existing machine learning and statistical models that power real-time credit decisions across acquisition, pricing, line assignment, and portfolio risk management. This is a high-impact opportunity to shape how we use data to drive responsible growth, improve financial inclusion, and optimize unit economics - all in a fast-paced, agile Banking environment. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week. You’ll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Requirements

5+ years of experience working with Python or R and SQL; familiarity with cloud environments like AWS, GCP, or Snowflake
5+ years of experience working with lending lifecycle and credit risk KPIs (e.g., delinquency, charge-offs, approval rates, ROI, CAC/payback)
4+ years of experience in credit risk modeling, ideally at a fintech, neobank, or consumer lending startup
4+ years of experience with both traditional statistical techniques (e.g., logistic regression, scorecards) and modern machine learning
Ability to travel 10%25

Nice To Haves

Experience with real-time underwriting or decisioning APIs
Work in embedded finance, BNPL, gig economy lending, or small business credit
Familiarity with explainability frameworks (e.g., SHAP, LIME)
Exposure to model governance or regulatory reviews
Comfort working in a dynamic, test-and-learn environment with incomplete data
Proven solid communication skills and attention to model transparency, fairness, and compliance

Responsibilities

Build Credit Models:
Develop and maintain predictive models for application and behavioral scoring, probability of default (PD), loss given default (LGD), and early risk detection.
Design models for underwriting, fraud detection, credit line management, and collections strategies
Feature Engineering & Data Innovation:
Create advanced features using alternative data sources (e.g., bank transaction data, health care claims data, cash flow, open banking, etc.)
Evaluate new data vendors and enrich internal datasets to improve model performance
End-to-End Ownership:
Take models from prototype to production in partnership with Data Engineering and Product teams
Build interpretable and compliant models aligned with FCRA, ECOA, and fair lending standards
Performance Monitoring & Iteration:
Own model monitoring, validation, reporting and back testing, regularly recalibrating for shifts in macroeconomic conditions or borrower behavior
Collaborate Cross-Functionally:
Work with Risk, Product, Compliance and Finance teams to integrate model outputs into strategy
Communicate insights clearly to technical and non-technical stakeholders