Data Scientist - Analytics CoE

We are looking for a hands-on Data Scientist / ML Engineer to build and deploy credit risk and decisioning models for an NBFC/banking environment. The role focuses on production-grade machine learning systems, including model development, deployment, scalability, and integration into decision engines. This is not a research-heavy AI role-the expectation is strong ownership of end-to-end pipelines, from data to production, with emphasis on robustness, performance, and business impact.

Key Responsibilities:

1. 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

2. 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.)

3. 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

4. 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

5. Cloud & Infrastructure

• Work on AWS-based environments (or equivalent cloud platforms)
• Understand distributed systems and compute optimization
• Optimize pipelines for performance and cost

6. Cross-functional Collaboration

• Work closely with: - Risk teams - Product / business stakeholders - Data engineering & platform teams
• Translate business requirements into scalable technical solutions