01Key Responsibilities
Credit Risk & Decisioning Models
Develop and maintain models for:
Credit risk (PD, delinquency, segmentation)
Customer behaviour (collections, engagement, response)
Income estimation / surrogate modelling (where applicable)
Work with bureau data (e.g., CIBIL/CRIF), transactional data, and alternative data sources
Translate business policies into model-driven decision frameworks
Data Engineering & Feature Pipelines
Build scalable feature pipelines using:
Python / PySpark / SQL
Handle large-scale datasets (structured + semi-structured)
Implement robust feature engineering (e.g., bureau features, exposure, EMI, leverage, etc.)
Model Deployment & Integration
Deploy models into production systems using:
Docker, Kubernetes, or similar containerization tools
Integrate models with decision engines / APIs
Ensure low-latency and high-throughput inference pipelines
MLOps, Testing & Monitoring
Implement:
Integration testing for model pipelines
Stress testing (performance under load)
Model monitoring (drift, stability, performance)
Work with DevOps teams to ensure reliable production systems
Handle versioning, rollback strategies, and model lifecycle management
Cloud & Infrastructure
Work on AWS-based settings (or equivalent cloud platforms)
Understand distributed systems and compute optimization
Optimize pipelines for performance and cost
Cross-functional Collaboration
Work closely with:
Risk teams
Product / business stakeholders
Data engineering & platform teams
Translate business requirements into scalable technical solutions
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