Note: The job is a remote job and is open to candidates in USA. UnitedHealth Group is hiring a Principal Data Scientist to develop, deploy, and monitor credit risk models for lending products. The role partners with Data Engineering, Product, Risk, Compliance, and Finance teams to improve underwriting, fraud detection, credit line management, and collections strategies.
Responsibilities
- Develop and maintain predictive models for application and behavioral scoring, probability of default (PD), loss given default (LGD), and early risk detection, ideally for commercial line of credit products
- Design models for underwriting, fraud detection, credit line management, and collections strategies
- 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
- 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
- Own model monitoring, validation, reporting and back testing, regularly recalibrating for shifts in macroeconomic conditions or borrower behavior
- Work with Risk, Product, Compliance and Finance teams to integrate model outputs into strategy
- Communicate insights clearly to technical and non-technical stakeholders
Skills
- Master's degree in a quantitative discipline (Statistics, Mathematics, Data Science, Economics, Computer Science, etc.)
- 5+ years experience working with Python or R and SQL; familiarity with cloud environments like AWS, Google Cloud Platform, 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 for commercial line of credit products
- 4+ years of experience with both traditional statistical techniques (e.g., logistic regression, scorecards) and modern machine learning
- 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
Benefits
- Flexibility to work remotely from anywhere within the U.S.
- Comprehensive benefits package (subject to eligibility requirements)
- Incentive and recognition programs (subject to eligibility requirements)
- Equity stock purchase (subject to eligibility requirements)
- 401k contribution (subject to eligibility requirements)
- Career development opportunities
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