Data Scientist Analytics Coe New Delhi (India)

We are looking for a hands-on Data Scientist / ML Engineer to build and deploy credit risk and decisioning

models for an NBFC/banking workplace. 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 solid ownership of end-to-end pipelines, from data to production,

with emphasis on robustness, performance, and business impact.

Key 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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