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).