Credit Risk Modelling

Role & responsibilities

• Extract, clean, and preprocess financial datasets for model development.
  
• Perform exploratory data analysis (EDA) to identify trends and variable relationships.
  
• Define bad definition, observation, and performance windows.
  
• Develop PD, LGD, and EAD models as per Basel III / IFRS9 guidelines.
  
• Build Application, Behavioral, and Collection scorecards for retail/wholesale portfolios.
  
• Apply statistical and machine learning techniques (Logistic Regression, Decision Trees, Random Forest, etc.).
  
• Conduct variable selection, transformation, and feature engineering.
  
• Evaluate model performance using KS, Gini, AUC, calibration, and stability tests.
  
• Perform out-of-sample and out-of-time validations.
  
• Document model methodology, assumptions, and development process.
  
• Ensure compliance with Basel, IFRS9, CCAR, and internal MRM frameworks.
  
• Support regulatory reviews, internal audit, and model validation queries.
  
• Implement model monitoring and recalibration processes.
  
• Track portfolio drift, PSI, and model performance metrics.
  
• Conduct root cause analysis for threshold breaches or degradation.
  
• Automate model monitoring and reporting dashboards using Python, SAS, or SQL.
  
• Prepare technical documentation and presentations for senior management.
  
• Collaborate with business, risk, data, and IT teams for model deployment.
  
• Maintain model inventory, change logs, and governance documentation.
  
• Stay updated on regulatory and quantitative modeling best practices.
  

Preferred candidate profile

• 10 years of experience in

  credit risk modeling, scorecard development, or risk analytics

  within

  banking or BFSI

  .
• Strong understanding of

  Basel II/III, IFRS9, CCAR, and CECL

  regulatory frameworks.
• Proven experience in developing

  PD, LGD, and EAD

  models for retail and wholesale portfolios.
• Hands-on proficiency in

  SAS, Python, SQL

  , and advanced

  statistical modeling

  techniques.
• Solid knowledge of

  statistical and machine learning methods

  (Logistic Regression, Decision Trees, Random Forests, etc.).
• Experience in

  model documentation

  ,

  validation support

  , and

  model performance monitoring

  .
• Familiarity with

  model governance, MRM standards (SR 11-7, ECB TRIM)

  , and audit processes.
• Exposure to

  IFRS9 impairment modeling

  and

  portfolio analytics

  (vintage, roll-rate, delinquency analysis).
• Strong quantitative and analytical skills with background in

  Statistics, Mathematics, Economics, or Quantitative Finance

  .
• Excellent communication and presentation abilities for stakeholder engagement.
  
• Certifications such as

  FRM, PRM, or CFA

  preferred.
• Experience working with

  global banking clients

  or in a

  regulatory-driven environment

  is highly