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Tier-1 brand, metro location, and mid-level title increase competition, but niche credit-risk focus reduces it.
Highly specialized credit-risk and regulatory modeling experience limits transferability across industries.
Explicit 3-8 years, mandatory credit-risk modeling/regulatory knowledge, and required coding skills raise strictness.
Develop, implement, enhance, and independently validate quantitative models related to credit risk, loss forecasting, provisioning, stress testing, capital, and other financial models.
Translate business and risk questions into analytical approaches and measurable model outcomes, including data preparation, methodology design, model performance assessment, and documentation.
Collaborate with business and risk stakeholders to refine requirements, explain model results, and support model implementation and governance.
3 to 8 years of experience in credit risk quantitative modeling and model development/validation (IRB, CECL, IFRS9, predictive modelling, forecasting).
Bachelor’s or master’s degree in Statistics, Mathematics, Economics, Finance, Engineering, Computer Science, Data Science, or quantitative discipline.
Proficiency in programming languages Python and SQL; experience with SAS, R, Spark, or cloud analytics environments is an advantage.
Knowledge of Model Risk Management frameworks (1LoD, 2LoD, 3LoD) and standards (SR 11-7, E-23, CP6-22/SS1-23) is required.
Experience working in banking, financial services, consulting, analytics GCCs, risk, finance, or advanced-analytics teams with financial-services use cases.
Strong analytical judgment with ability to challenge model assumptions and focus on practical business impact.
Comfortable working independently and collaboratively in a fast-paced, global consulting environment, including cross-time zone collaboration and occasional travel.