Data Scientist

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

Eligibility Criteria for the Job

Experience

4+ years of hands-on experience in data science, with a focus on generative AI and hyper-personalization

Education

Bachelors/masters in computer science, Engineering, Data Science, ML, Statistics, or related field

Skills & Competencies

• Proficiency in programming languages like Pyspark, Python, R, or Java.
• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Strong knowledge of data processing libraries (e.g., Pandas, NumPy, Scikit-learn, polars).
• Familiarity with cloud platforms (AWS, GCP, Azure) for deploying models and managing data pipelines.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).