Director of Lending & Credit Risk Analytics (Alpharetta)
Artemis Consultants, Alpharetta, GA, United States
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
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