Credit Risk Model Validation Analyst / Senior Analyst

Role Summary
We are looking for a Credit Risk Model Validation professional with 2–5 years of experience in credit cards, consumer lending, retail finance, banking or fintech. The person will independently review credit-risk models, test their performance, challenge key assumptions and clearly communicate any risks or limitations to business and governance teams.
Key Responsibilities
Validate underwriting, acquisition, behaviour, collections, fraud and portfolio-risk models.
Review the full model lifecycle, including:
Business purpose and intended use
Data quality and sample selection
Variable selection and segmentation
Model methodology and assumptions
Model implementation
Ongoing performance monitoring
Assess models developed using Logistic Regression, scorecards, Decision Trees, Random Forest, Gradient Boosting and XGBoost.
Perform AUC/Gini, KS, PSI, calibration, back-testing, benchmarking, sensitivity, stress-testing and stability analysis.
Evaluate model performance across vintages, score bands, credit-line bands, acquisition channels and customer segments.
Investigate population drift, performance deterioration and areas of emerging risk.
Compare development code and documentation with the production implementation to confirm that the model is working as intended.
Review model limitations, monitoring thresholds, compensating controls and remediation plans.
Prepare clear validation reports covering the observation, business impact, root cause, recommendation and severity.
Support model inventory, risk classification, periodic monitoring and governance reporting.
Present validation results to model developers, business stakeholders, senior management and partner banks.
Required Qualifications
Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Engineering, Data Science, Finance or a related field.
2–5 years of experience in model development, model validation, model monitoring or credit-risk analytics.
Hands-on experience with Python or SAS and SQL.
Good understanding of credit-risk modelling, model performance, calibration and stability.
Familiarity with vintage, delinquency, roll-rate and charge-off analysis.
Ability to challenge model assumptions and explain technical findings in clear business language.
Strong analytical, documentation and communication skills.
Preferred Experience
Experience in US credit cards, consumer lending, subprime or near-prime portfolios.
Experience with underwriting, application-risk, behaviour, collections or fraud models.
Understanding of model-risk-management principles such as SR 11-7 and OCC guidance.
Exposure to machine-learning explainability techniques such as SHAP and PDP.
Familiarity with Redshift, Tableau, Git or cloud-based analytical environments.
What We Are Looking For
We need someone who is comfortable working independently, asking the right questions and going beyond simply calculating model-performance metrics. The successful candidate should be able to identify what is going wrong, explain why it matters to the business and recommend practical actions to address the risk.

Work arrangement
No