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Data Scientist - Analytics CoE at Hero Fincorp — Delhi

Experience4–7 yrs
SalaryNot disclosed
Work modeonsite
Levelmid
Posted10 Sept 2026

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About Hero Fincorp

Hero Fincorp is hiring in Delhi in financial services. This role looks for around 4+ years of experience.

Skills

credit risk modeling
machine learning
Python
PySpark
SQL
feature engineering
Docker
Kubernetes
model deployment
decision engines
API integration
integration testing
stress testing
model monitoring
MLOps
AWS
distributed systems

The role

A data scientist at a financial services company develops credit risk models and production machine learning systems, building Python and PySpark pipelines for decision engines. Model deployment, AWS, and model monitoring support scalable, low-latency inference and robust business decisioning.

Full 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.