Hero Fincorp - 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