Credit Risk Analyst

Role Overview:

Credit Risk analytics

Key Responsibilities:

• Develop, validate, and enhance

  credit risk models

  (PD, LGD, EAD, scorecards, underwriting models, early warning models, etc.).
• Build and deploy

  machine learning models

  for credit decisioning, customer segmentation, fraud detection, and risk forecasting.
• Analyze credit portfolio performance to identify risk patterns, portfolio trends, and actionable insights.
• Work closely with product, underwriting, policy, and engineering teams to implement models into production.
• Conduct

  data exploration, feature engineering, and model performance monitoring

  using large and complex datasets.
• Ensure adherence to

  regulatory standards

  , model governance guidelines, and documentation requirements.
• Collaborate with risk and compliance teams to ensure models meet audit, regulatory, and internal risk management expectations.
• Create comprehensive model documentation including methodology, assumptions, performance metrics, and validation results.

Required Skills & Experience:

• 48 years of experience in

  Credit Risk Analytics

  ,

  Risk Modeling

  , or

  Data Science

  within financial services or fintech.
• Strong hands-on experience in

  ML model development

  , including regression, classification, time series, and ensemble techniques.
• Proficiency in

  Python

  or

  R

  , SQL, and data processing frameworks.
• Solid understanding of

  credit lifecycle

  , scorecards, bureau data, demographic/behavioral data, and financial risk indicators.
• Experience working with

  large datasets

  and cloud platforms (AWS, GCP, or Azure).
• Strong knowledge of

  model validation, monitoring, and governance frameworks

  .
• Excellent analytical reasoning, problem-solving, and communication skills.

Preferred Qualifications:

• Experience with

  retail lending, unsecured lending, BNPL, credit cards, or SME lending

  .
• Exposure to

  MLOps

  , model deployment, and API integration frameworks.
• Familiarity with regulatory guidelines (e.g., RBI, Basel norms, IFRS9).
• Background in

  statistics, mathematics, computer science, data science

  , or related fields.