Senior 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 risk modeling
  • credit scorecards
  • Python
  • PySpark
  • scikit-learn
  • XGBoost
  • LightGBM
  • CatBoost
  • Random Forest
  • neural networks
  • feature engineering
  • model validation
  • model governance
  • model monitoring
  • portfolio analytics
  • credit underwriting
  • statistical evaluation
  • hypothesis testing
  • sampling theory
  • classification
  • large structured financial datasets
  • scalable training pipelines
  • production deployment

The role

A data scientist at a retail lending bank develops credit risk models and underwriting strategies using statistical modeling, machine learning, and Python. Credit scorecards, portfolio analytics, and model monitoring support scalable decisioning and portfolio profitability.

Full job description

We are seeking an experienced Credit Risk 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.