Assistant Manager/ Manager-Credit Risk

EXL; Gurugram District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

EXL is seeking a Credit Risk Modeler to develop, validate, and maintain risk models used to assess borrower and counterparty creditworthiness. The role collaborates with risk, data science, and business teams to ensure models meet regulatory requirements and align with risk appetite.

The ideal candidate has 2+ years in credit risk modeling, strong analytics, and programming skills in Python, R, SAS, and SQL, with experience in PD, LGD, EAD, CVA and IFRS 9 Basel guidelines.

Qualifications

• 2+ years of experience in credit risk modeling or related fields within banking or finance.
• Strong background in statistical modeling and machine learning for credit risk.
• Proficiency in Python, R, SAS and SQL; familiarity with Basel III and IFRS 9 guidance.

Responsibilities

• Develop and implement credit risk models such as PD, LGD, EAD, and CVA.
• Create scoring, rating models and stress testing frameworks.
• Validate models, perform back testing and monitor performance.
• Prepare reports on model performance and regulatory compliance.

Skills

Credit risk modeling

Statistical modeling

Machine learning

Python

R

SAS

SQL

Back testing

Communication

Tools

Python

R

SAS

SQL

MATLAB

Job description

The Credit Risk Modeler will be responsible for developing, validating, and maintaining credit risk models that are used to assess the creditworthiness of borrowers and counterparties. This role is critical to the financial institution's risk management framework, ensuring that the models meet regulatory requirements, align with business objectives, and help in managing credit risk effectively. The modeler will work closely with other risk professionals, data scientists, and business stakeholders to ensure that credit risk models are accurate, robust, and aligned with the bank’s risk appetite.

Key Responsibilities:

• Develop and implement credit risk models, including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and Credit Valuation Adjustment (CVA).
• Create and maintain scoring models, credit rating models, and stress testing frameworks.
• Apply statistical and machine learning techniques to develop predictive models using large datasets.

Model Validation and Performance Monitoring:

• Validate existing credit risk models to ensure they are accurate, up-to-date, and compliant with regulatory standards.
• Perform back testing and stress testing to assess model performance under various market conditions.
• Monitor the ongoing performance of models and recommend adjustments as needed to improve accuracy and reliability.

Data Analysis and Reporting:

• Analyze historical credit data to identify trends, patterns, and factors influencing credit risk.
• Prepare and present reports on model performance, risk exposures, and potential losses to senior management and other stakeholders.
• Work with IT and data teams to ensure data integrity and availability for model development.

Regulatory Compliance:

• Ensure credit risk models comply with internal policies and regulatory requirements (e.g., Basel III, IFRS 9, or other applicable standards).
• Participate in regulatory reviews and audits of credit risk models and provide necessary

Experience:

• 2+ years of experience in credit risk modeling, quantitative analysis, or related fields within banking, finance, or risk management.
• Strong experience with statistical modeling, machine learning, and programming (e.g., R, Python, SAS, SQL).
• Familiarity with credit risk metrics (e.g., PD, LGD, EAD, CVA) and credit risk modeling methodologies.
• Experience with regulatory requirements for credit risk models (e.g., Basel, IFRS 9) is preferred.

Skills:

• Strong analytical and problem-solving skills.
• Expertise in statistical and machine learning techniques such as regression, time series analysis, classification, and clustering.
• Advanced proficiency in programming languages (e.g., Python, R, SQL).
• Familiarity with risk management software and tools (e.g., SAS, SQL, MATLAB, or others).
• Excellent communication skills, both written and verbal, with the ability to present complex concepts to non-technical stakeholders.