Data Scientist

IDFC FIRST Bank · Mumbai

  • Experience2–6 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelmid
  • Posted2 Sept 2026
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About IDFC FIRST Bank

IDFC FIRST Bank is hiring in Mumbai in financial services. This role looks for around 2+ years of experience.

Skills

  • statistical modeling
  • machine learning
  • credit modeling
  • model governance
  • model monitoring
  • statistics
  • hypothesis testing
  • sampling theory
  • classification
  • Python
  • PySpark
  • scikit-learn
  • XGBoost
  • LightGBM
  • CatBoost
  • Random Forest
  • neural networks
  • feature engineering
  • portfolio analytics
  • credit underwriting
  • model validation
  • production deployment

The role

A data scientist at a retail lending institution develops credit risk models and underwriting strategies, applying statistical modeling and machine learning to behavioral scorecards and portfolio decisions. The work centers on Python, PySpark, and credit modeling, with ownership of model validation, deployment, monitoring, and optimization.

Full job description

We are seeking an experienced Data Analyst/Scientist to lead the development of ML-driven applications and behavioural scorecards for retail lending portfolios. The role requires strong statistical rigour, end-to-end model ownership, and the ability to translate credit risk appetite into scalable data-driven strategies.

Responsibilities:

Design and deploy statistical models, including but not limited to application and behavioural scorecards, using advanced ML techniques.

Lead end-to-end lifecycle: problem framing, data extraction, feature engineering, model development, validation, deployment, and monitoring.

Develop statistically robust models using techniques such as (but not limited to): Gradient Boosting (XGBoost, LightGBM, CatBoost), Random Forest, ensemble methods, and neural networks (MLP).

Apply rigorous evaluation frameworks (AUC, KS, Gini, Lift, PSI, stability analysis).

Address imbalanced datasets, reject inference, segmentation, and portfolio drift.

Build challenger models and drive continuous performance optimisation.

Develop and refine credit underwriting strategies aligned to risk appetite, approval rate targets, and portfolio profitability.

Conduct portfolio analytics and recommend cut-off, limit, and pricing strategies.

Collaborate with Risk, Policy, Product, and Tech teams to translate business requirements into deployable decisioning frameworks.

Ensure robust monitoring, back-testing, and periodic recalibration.

Requirements:

Bachelor's in engineering, business, analytics, data science, or a related field.

Technical Requirements:

Strong foundation in statistics, hypothesis testing, sampling theory, and classification methodologies.

Proven experience in ML-based credit modeling, model governance, and monitoring.

Expertise in handling large structured financial datasets.

Proficiency in Python, PySpark, Scikit-learn, and boosting frameworks.

Experience building scalable training pipelines and supporting production deployment.