[Hiring] Principal Data Scientist @UnitedHealth Group

Role Description

You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

• 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, ideally for commercial line of credit products.
  • 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.

Qualifications

• 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, 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 for commercial line of credit products.
• 4+ years of experience with both traditional statistical techniques (e.g., logistic regression, scorecards) and modern machine learning.

Requirements

• 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

• Comprehensive benefits package.
• Incentive and recognition programs.
• Equity stock purchase.
• 401k contribution (all benefits are subject to eligibility requirements).

Application Deadline

This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.