Manager

To lead the development, enhancement, validation, and implementation of advanced quantitative models for credit risk assessment, Expected Credit Loss (ECL) calculation, and stress testing. This role requires deep expertise in financial modeling, regulatory compliance (IFRS 9, CECL, Basel III), and strong collaboration with various financial and technical teams to ensure robust risk management and reporting at Uniqus.

Key Responsibilities:

Model Development & Enhancement:

• Develop and implement advanced quantitative models for assessing credit risk, including models for Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
• Work with large datasets to develop models that estimate credit risk exposure, focusing on accuracy, robustness, and predictive power.
• Design and refine models to calculate Expected Credit Loss (ECL) under IFRS 9 and CECL frameworks, ensuring compliance with accounting and regulatory standards.
• Continuously monitor and enhance existing models based on new data, market conditions, and regulatory changes.

Model Validation & Backtesting:

• Validate and backtest credit risk models, ensuring they meet the required standards for accuracy, consistency, and reliability.
• Develop validation frameworks for assessing model performance and ensuring models accurately reflect credit risk under different economic conditions.
• Provide documentation of model assumptions, methodologies, and limitations, ensuring transparency and compliance with internal governance and regulatory requirements.
• Conduct periodic performance reviews and backtesting of models to ensure they continue to perform well over time and align with actual credit losses.

Expected Credit Loss (ECL) Modeling:

• Lead the development of ECL models, calculating the credit loss provisions for financial instruments in line with IFRS 9 or CECL.
• Develop robust methodologies for estimating PD, LGD, and EAD at a granular level (e.g., loan type, sector, region), while incorporating macroeconomic factors.
• Collaborate with accounting, finance, and credit risk teams to integrate ECL models into the firm's risk management and financial reporting processes.
• Ensure that the ECL models reflect appropriate segmentation of portfolios and the application of relevant adjustments for forward-looking information.

Credit Risk Stress Testing:

• Lead the design and execution of credit risk stress testing processes to assess the resilience of credit portfolios under extreme but plausible scenarios.
• Develop and implement stress testing models for key credit risk metrics such as PD, LGD, and EAD under different macroeconomic and market scenarios.
• Collaborate with other risk teams to define relevant stress test scenarios, including both regulatory scenarios (e.g., CCAR, EBA) and internal stress scenarios.
• Analyze stress test results and present findings to senior management, providing actionable insights on portfolio risk, capital adequacy, and mitigation strategies.

Model Governance & Regulatory Compliance:

• Ensure that all credit risk models adhere to regulatory requirements, including Basel III, IFRS 9, CECL, and other relevant global or local regulations.
• Contribute to internal and external model audits and provide clear documentation on model assumptions, validation processes, and limitations.
• Stay up-to-date with regulatory changes and industry best practices to ensure models remain compliant with evolving standards and regulations.
• Assist in the preparation of regulatory reports and disclosures related to credit risk modeling and stress testing.

Collaboration & Stakeholder Engagement:

• Collaborate with other departments (e.g., credit risk, finance, accounting, and IT) to ensure that models are integrated effectively into day-to-day operations and decision-making.
• Provide support to senior management in understanding model outcomes, advising on risk mitigation strategies, and supporting strategic decision-making.
• Provide expert advice on the application and interpretation of credit risk models, both to technical teams and non-technical stakeholders.

Qualifications:

• Education:

  Masters or PhD in Quantitative Finance, Financial Engineering, Mathematics, Statistics, or a related field. Professional certifications (e.g., CFA, FRM) are a plus.

Experience:

• Minimum of 3-5 years of experience in quantitative risk modeling, with a focus on credit risk, PD, LGD, EAD models, and stress testing.
• Proven experience with the development and validation of ECL models under IFRS 9, CECL, or similar regulatory frameworks.
• Hands-on experience with credit risk stress testing methodologies and model implementation.
• Strong background in working with large financial datasets and using statistical modeling techniques to assess and manage risk.

Technical Skills:

• Proficiency in programming languages such as Python, R, MATLAB, or SAS for model development and data analysis. Candidates with proficiency in VBA, Python, SAS will be preferred.
• Strong experience with data analysis tools (e.g., SQL, Excel, Hadoop, Spark) and machine learning libraries (e.g., scikit-learn, TensorFlow).
• Familiarity with credit risk management platforms (e.g., Moody's, S&P, Bloomberg) and risk analytics tools.
• Strong understanding of financial statements, credit ratings, and macroeconomic factors influencing credit risk.