Hero FinCorp
Website:
herofincorp.com
Job details:
We are seeking a hands-on Data Scientist / ML Engineer to design, build, and deploy credit risk and decisioning models within an NBFC/banking environment. This role is focused on production-grade machine learning systems, encompassing model development, deployment, scalability, and integration with decision engines.
This is not a research-focused AI role. The expectation is strong ownership of end-to-end ML pipelines, from data ingestion and feature engineering to deployment and monitoring, with a strong emphasis on robustness, performance, and measurable business impact.
KEY RESPONSIBILITIES
1. Credit Risk & Decisioning Models
- Develop and maintain models for:
- Credit risk (Probability of Default, delinquency prediction, segmentation)
- Customer behaviour (collections, engagement, response modeling)
- Income estimation and surrogate modeling (where applicable)
- Work with bureau data (e.g., CIBIL, CRIF), transactional data, and alternative data sources
- Translate business policies and risk rules into model-driven decision frameworks
2. Data Engineering & Feature Pipelines
- Build scalable and reusable feature pipelines using Python, PySpark, and SQL
- Process and manage large-scale structured and semi-structured datasets
- Implement robust feature engineering, including bureau features, exposure metrics, EMI, leverage ratios, and related financial indicators
3. Model Deployment & Integration
- Deploy machine learning models into production using Docker, Kubernetes, or similar containerization tools
- Integrate models with decision engines and API-based systems
- Design and maintain low-latency, high-throughput inference pipelines
4. MLOps, Testing & Monitoring
- Implement:
- Integration and pipeline testing
- Stress and performance testing under production load
- Model monitoring for drift, stability, and predictive performance
- Collaborate with DevOps teams to ensure system reliability and scalability
- Manage model versioning, rollback strategies, and end-to-end lifecycle management
5. Cloud & Infrastructure
- Work in AWS-based environments (or equivalent cloud platforms)
- Understand distributed systems and compute optimization techniques
- Optimize ML pipelines for performance, scalability, and cost efficiency
6. Cross-functional Collaboration
- Work closely with:
- Risk and credit teams
- Product and business stakeholders
- Data engineering and platform teams
- Convert business requirements into scalable, production-ready technical solutions
ELIGIBILITY CRITERIA
Experience
- 4+ years of hands-on experience in Data Science / ML Engineering
- Experience in generative AI and hyper-personalization is a plus
Education
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Machine Learning, Statistics, or a related field
Skill and Competencies
- Proficiency in Python, PySpark, R, or Java
- Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras
- Strong knowledge of data processing libraries (Pandas, NumPy, Scikit-learn, Polars)
- Familiarity with cloud platforms (AWS, GCP, Azure) for model deployment and data pipelines
- Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau
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