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About the Role
Perform independent end-to-end validation of credit risk, finance, and other models. Rigorously review and challenge all aspects of model development, including conceptual soundness, data integrity, feature engineering, regulatory compliance, and documentation. Collaborate with data scientists and ML engineers to understand business context and communicate model risks. Provide actionable recommendations and document validation outcomes. Drive the enhancement of AI tools for model validation. Stay updated on credit and finance modeling trends and AI/ML technologies. Maintain robust model risk management frameworks.
Responsibilities
- Perform independent end-to-end validation of credit risk, finance, and other models.
- Rigorously review and challenge conceptual soundness, data integrity, feature engineering, regulatory compliance, and documentation.
- Independently replicate model development processes and conduct challenger analyses.
- Collaborate with first-line data scientists, ML engineers, and product stakeholders.
- Understand models' business context and ensure transparent communication of model risks.
- Provide actionable recommendations and formally document validation outcomes.
- Drive continuous enhancement of agentic AI tools for model validation.
- Stay up-to-date with emerging trends in credit and finance modelling and AI/ML technologies.
- Maintain robust model risk management frameworks, policies, and procedures.
Requirements
- Advanced degree (Master’s or PhD) in a quantitative field such as data science, statistics, mathematics, computer science, physics, or engineering; or equivalent experience.
- 3+ years of hands-on experience in credit risk and/or IFRS9/CECL impairment modeling.
- Strong technical expertise in statistical and machine learning models.
- Deep understanding of credit risk and/or IFRS9/CECL provisioning models.
- Hands-on experience with programming languages and tools commonly used in data science, such as Python, SQL, Spark, and AWS.
- Excellent analytical, problem-solving, and decision-making abilities.
- Passion for innovation and staying at the forefront of data science and risk management.
- Strong communication and stakeholder management skills.
- Ability to convey complex technical information to non-technical audiences.
- Knowledge of regulatory requirements and expectations for model risk management.
Skills
- Statistical models
- Machine learning models
- Credit risk modeling
- IFRS9/CECL provisioning models
- Python
- SQL
- Spark
- AWS
- Analytical skills
- Problem-solving skills
- Decision-making abilities
- Communication skills
- Stakeholder management skills
- Model risk management
Experience Level
- 3+ years of hands-on experience
Education Level
- Master’s degree
- PhD degree