Data Scientist

Key Responsibilities

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

Last Reviewed: July 2025

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/master’s 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