Data Scientist - Analytics CoE

Hero Fincorp; New Delhi; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Hero Fincorp is seeking a hands-on Data Scientist / ML Engineer to build and deploy credit risk and decisioning models in a fast-paced NBFC/banking environment.

You will own end-to-end pipelines from data to production, focusing on robustness and performance, with production‑grade systems, scalable feature pipelines, and seamless model deployment using Docker/Kubernetes on AWS, delivering measurable business impact.

Qualifications

• Hands-on experience building production-grade ML systems.
• Experience deploying ML models into production.
• Ability to build end-to-end data pipelines from data to deployment.
• Strong focus on robustness, performance, and business impact.

Responsibilities

• Develop and maintain credit risk and decisioning models.
• Build scalable feature pipelines using Python, PySpark and SQL.
• Deploy models into production and integrate with APIs.
• Implement MLOps, testing, and monitoring for models.
• Work with cloud platforms (AWS) and ensure low-latency inference.
• Collaborate with risk, product, data engineering, and platform teams.

Skills

Python

PySpark

SQL

Docker

Kubernetes

MLOps

API integration

Tools

AWS

Job description

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.