Model Risk Analyst
About the role
CRDB Bank Plc seeks a Model Risk Analyst in the Data Management Office at its Tanzania Head Office. The role assesses and challenges the fitness, limitations and risks of AI and machine learning models throughout their lifecycle, performs validation separate from model development, and tracks findings under the Manager, AI Governance and Model Risk. Responsibilities: - Maintain a risk-based validation schedule covering new models, material changes, periodic reviews and third-party systems. - Agree intended use, scope, acceptance criteria and required evidence with stakeholders before validation begins. - Examine data provenance, representativeness, labels, feature timing and development/testing separation, and identify leakage and unsupported assumptions. - Reproduce performance calculations and compare results with benchmarks, business thresholds and customer or channel segments. - Test model stability, calibration, explainability, fairness and sensitivity. - Evaluate generative AI outputs for factual accuracy, grounding, harmful bias, robustness and misuse, including prompt-injection scenarios. - Assess vendor model evidence critically and identify limitations from restricted access or unverified supplier claims. - Produce evidence-linked validation reports with findings, severity and recommendations. - Track remediation, retest findings, and escalate unresolved material risks and overdue actions. - Review ongoing model performance, drift, incidents and overrides, and recommend revalidation, retraining or retirement where needed. How to apply: Search for the vacancy title, register or sign in, and submit your CV and relevant supporting documents before the deadline. Apply online through the CRDB Bank careers portal: https://careers.crdbbank.co.tz/jobs
Key requirements
- Bachelor's degree in Statistics, Mathematics, Data Science, Computer Science, Econometrics, Risk Management, Quantitative Finance, or a related field.
- A Master's degree in a related field is an added advantage.
- Minimum of 3 years of experience in model validation, quantitative analysis or model risk.
- Certifications such as FRM, PRM, or in model risk, data science or AI are an added advantage.
- Experience validating machine learning, predictive, statistical, scorecard or forecasting models in regulated or enterprise environments.
- Knowledge of model risk management frameworks, validation methodologies and model lifecycle management.
- Working proficiency in Python or R and SQL, and the ability to reproduce model results.
- Knowledge of statistical testing, calibration, stability, explainability, bias and responsible AI principles.
- Model Validation
- Model Risk Management
- Python
- R