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Tier-1 brand, mid-level generalist role, metro location, and common 3–6 year band increase candidate competition.
Specialized consumer auto credit decisioning and underwriting experience makes cross-industry transferability low.
Explicit 4–7 year requirement plus mandatory retail credit domain experience and Python/SQL skills raise strictness.
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Analyze consumer and dealer/channel credit performance to inform strategy recommendations impacting approval rate, booked volume, loss rate, and profitability.
Develop, maintain, and test credit decisioning strategies balancing risk, growth, and P&L trade-offs; support credit policy execution and governance.
Collaborate cross-functionally to implement strategy changes, monitor portfolio trends and risks, and communicate insights to US stakeholders for portfolio optimization and process improvements.
4–7 years of experience in retail/auto credit decisioning, underwriting, or credit policy execution in banking, fintech, or credit analytics.
Proficiency in Python and SQL for data extraction, aggregation, analysis, and synthesis into recommendations.
Strong understanding of consumer credit risk concepts and how underwriting/decisioning affects portfolio outcomes (approval rate, loss rate, growth, profitability).
Experience working with US-based stakeholders and operating across time zones; excellent written and verbal communication skills.
Experienced in hands-on credit data analytics and strategy development with ability to balance risk and profitability.
Comfortable designing and evaluating challenger rules/testing and structured experimentation in credit decisioning.
Proven capability in cross-functional collaboration and communicating analytic insights clearly to senior US stakeholders.