Credit Risk Modelling

1 day ago

Delhi Mumbai NCR Pune, India OM Careers Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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

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