Credit Risk Analyst

Role Overview : We are seeking a highly skilled professional with strong expertise in Credit Risk analytics and Machine Learning (ML) model development . The ideal candidate will be responsible for building, validating, and optimizing predictive models that support credit decisioning, risk assessment, and portfolio monitoring across the lending lifecycle. This role requires deep analytical capabilities, strong statistical knowledge, and hands-on experience working with large-scale financial datasets.

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 :

4–8 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.