Director of Lending & Credit Risk Analytics

The Director of Lending & Credit Risk Analytics is a senior, customer-facing analytics leader within the Data Science organization. This role owns lending and credit-risk interpretation across the company, with a primary focus on non-prime and alternative credit use cases.

Experience & Domain Expertise

• 7–10+ years of experience in lending, credit risk, fraud analytics, or financial risk
• Direct experience supporting banks, fintech lenders, or non‑prime / alternative credit providers
• Deep understanding of underwriting and credit policy design, fraud detection techniques, portfolio monitoring, and early‑risk indicators
• Familiarity with regulatory and compliance considerations relevant to lending

Analytical & Technical Judgment

• Does not need to be a career data scientist, but must command technical credibility when engaging with senior data scientists, risk modelers, and quantitative teams
• Able to challenge, defend, and refine analytical conclusions in technical discussions without defaulting to theory or abstraction
• Demonstrated ability to distinguish meaningful, decision‑relevant signals from noise or false positives
• Proven ability to evaluate model performance in terms of real‑world business impact, not just statistical metrics
• Ability to combine quantitative outputs with real‑world lending judgment to produce conclusions that withstand scrutiny from both technical and business stakeholders

Leadership & Communication

• Strong ability to translate complex analytics into clear, lender‑ready narratives
• Proven track record telling believable, defensible risk stories aligned to real underwriting, fraud, and portfolio behaviors
• Prior people leadership experience or clear readiness to manage and mentor others
• Confident communicator with executives, customers, and internal stakeholders; willing and able to push back when analytics do not reflect real‑world risk behavior

What Success Looks Like

• Increased lender trust and confidence in the analytics
• Consistent delivery of clean, defensible, decision‑relevant customer analyses
• Reduced dependency on senior analytics leadership for lending interpretation
• Clear separation between customer‑facing analytics and R&D model development
• Strong alignment between analytics, product, and go‑to‑market teams

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