Credit Risk Manager
7 days ago
About the job Credit Risk Manager
About the Role
Our client's Credit Risk Practice partners with leading banks, fintechs, and financial institutions to design and implement advanced risk management strategies. We are seeking a Credit Risk data scientist to support the design, execution, and monitoring of credit strategies across the lending lifecycle. This role is highly analytical, with direct involvement in underwriting models and policies, line assignment, credit line increase programs, and ongoing portfolio monitoring.
Key Responsibilities
- Contribute to development and validation of underwriting models, including data preparation, feature engineering, and performance benchmarking.
- Support policy design for credit, fraud, pricing, and digital pre-qualification strategies.
- Work on line assignment and credit line increase strategies, optimizing risk/return tradeoffs and supporting income capture initiatives.
- Assist in graduation and product upgrade strategies for profitable base (like engaged, low-risk customers).
- Support the design of risk appetite frameworks, concentration limits, and program guardrails.
- Help establish reporting, monitoring dashboards, success metrics, and participate in industry benchmarking.
- Collaborate with cross-functional teams to ensure strategies are aligned with business goals, compliant with policies, and scalable.
- Drive innovation by testing new data sources, exploring A/B testing approaches, and refining strategies through continuous learning.
- Bachelors degree in Business, Finance, Statistics, Mathematics, Engineering, or related field; Masters preferred.
- 5+ years of experience in credit risk analytics, modeling, or strategy within banking, fintech, or consulting.
- Strong knowledge of the credit lifecycle (underwriting, line assignment, CLI, monitoring, risk appetite).
- Proficiency in SQL and SAS; exposure to Python /Tableau or other analytical tools is a plus.
- Experience with statistical modeling, segmentation, regression, and hyperparameter optimization.
- Knowledge of model governance, regulatory guidelines and designing risk appetite framework.
- Strong analytical problem-solving skills with ability to translate insights into actionable strategies with excellent communication and presentation skills.
- Ability to work in fast-paced environments, manage multiple priorities, and collaborate across teams.
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