About the Role:
We are seeking a Senior Credit Risk Analyst with strong unsecured lending experience to support client’s consumer credit products. This role will focus on credit card and personal loan portfolios, leveraging data to drive risk strategies, optimize underwriting, and improve portfolio performance. You will work closely with cross-functional partners in Risk, Product, Data Science, and
Engineering to design, implement, and monitor credit risk strategies that balance growth, risk, and customer experience.
Required Qualifications:
- Bachelor’s degree in a quantitative field (e.g., Statistics, Mathematics, Economics, Engineering, Computer Science, Finance) or equivalent practical experience.
- 5+ years of hands-on experience in Credit Risk within unsecured lending, ideally in:
- Credit cards (strongly preferred), and/or Personal loans or other unsecured consumer lending products.
- Strong SQL skills with demonstrated experience querying and manipulating large, complex datasets.
- Proficiency in Python for data analysis, modeling support, and automation (e.g., pandas, NumPy, basic visualization libraries).
- Proven track record of using analytics to solve business problems in credit risk (e.g., underwriting, line management, pricing, collections, fraud/risk trade-offs).
- Solid understanding of core credit risk concepts: scorecards, cutoffs, PD/LGD/EAD, vintage analysis, loss curves, risk-based pricing, and portfolio segmentation.
- Strong problem-solving skills with the ability to structure ambiguous problems, form hypotheses, and drive analyses end-to-end.
- Excellent communication skills, with the ability to translate complex analytical findings into clear, actionable recommendations for non-technical stakeholders.
Preferred Qualifications:
- Experience working in a fintech, payments, or digital lending environment.
- Familiarity with credit bureau data and alternative data sources.
- Experience with experimentation (A/B testing), champion–challenger frameworks, and test design.
- Exposure to machine learning–driven risk models and their application in production environments.
- Experience with BI/visualization tools (e.g., Tableau, Power BI, Looker) for dashboarding and reporting.
- Master’s degree in a quantitative discipline is a plus.