Chief Model Strategist

2 days ago

Vadodara, Gujarat, India beBeeRisk Full time ₹ 80,00,000 - ₹ 1,50,00,000
Job Title:

Lead Risk Modeller

About the Role

We are seeking an experienced and analytically driven professional to join our Credit Ratings team as a Lead Risk Modeller.

Main Responsibilities
  • Design, implement, and refine statistical and econometric models to support credit rating methodologies.
  • Develop models for key risk parameters, including Probability of Default (PD), Loss Given Default (LGD), and Expected Loss (EL).
  • Conduct performance testing, benchmarking, and sensitivity analysis of existing models.
  • Implement validation protocols to ensure models meet internal standards and regulatory expectations.
  • Analyse large datasets from multiple sources to support model calibration and back-testing.
  • Perform exploratory data analysis and statistical testing to inform model assumptions.
  • Prepare comprehensive model documentation for internal committees, model governance teams, and regulators.
  • Communicate complex quantitative findings clearly to non-technical stakeholders.
  • Liaise with cross-functional teams including Ratings, IT, Compliance, and Data Engineering.
  • Provide analytical support during credit rating committee discussions and reviews.
Candidate Profile
  • Bachelor's Degree in Commerce / Finance / Statistics / Mathematics / Data Science or related field.
  • Master's Degree in Finance / Statistics / Mathematics / Data Science (Preferred)
  • 4–6 years of experience in credit risk modelling, model validation, or quantitative analysis within a credit rating agency, bank, or financial institution.
  • Strong command of programming tools such as Python, R, SAS, and SQL.
  • Practical understanding of credit risk models and financial instruments.
  • Familiarity with regulatory frameworks like Basel III/IV.
  • Excellent quantitative and analytical skills with strong attention to detail.
  • Ability to manage multiple projects with minimal supervision.
  • Exposure to machine learning techniques applied to credit risk or financial modelling.
  • Experience with data visualization tools.
  • Working knowledge of rating agency methodologies and capital markets.

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