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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.