Data Scientist (Fraud & Ops Analytics)

IDFC FIRST Bank · Mumbai

  • Experience5–9 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelsenior
  • 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 5+ years of experience.

Skills

  • machine learning
  • statistical techniques
  • econometric techniques
  • Python
  • SQL
  • Pandas
  • NumPy
  • scikit-learn
  • TensorFlow
  • PyTorch
  • regression
  • classification
  • anomaly detection
  • evaluation metrics
  • data strategy
  • model training pipelines
  • machine learning systems
  • machine learning pipelines
  • fraud identification
  • unbalanced datasets

The role

A data scientist at a banking and fintech company develops fraud detection models using machine learning, statistical modeling, and anomaly detection. This person builds Python and SQL analytics pipelines for risk mitigation and operational strategy.

Full job description

The position is in the Fraud and Operational Analytics Team. This team is mainly responsible for creating machine learning/AI solutions for effective fraud prevention and driving operational scalability to improve customer experience, reduce operational cost, risk mitigation, etc. The role will focus on building cutting-edge ML solutions.

Responsibilities:

Analyse large amounts of data to derive business insights and create innovative solutions.

Understanding business nuances and associated fraud patterns.

Develop advanced ML models for fraud identification.

Innovate with a focus on better and newer approaches.

Explore alternate data sources that can add value on top of traditional data sources.

The role requires exhibiting a high level of expertise in data strategy and model training pipelines.

She/He will drive insights in generating data-driven, actionable strategies.

Supporting the business and risk teams with bespoke and strategic analysis.

Requirements:

Proficiency and experience in econometric, statistical, and machine learning techniques.

Proficiency in Python, SQL, Pandas, Numpy, Sklearn, Tensorflow/PyTorch, etc.

Strong understanding of statistical concepts and modelling techniques for regression, classification, and anomaly detection.

Good understanding of evaluation metrics.

Logical thought process and ability to scope out an open-ended problem into a data-driven solution.

MBA, Master of Economics, and Master of Statistics from top-tier colleges.

3-4 years of experience in the fintech and banking domains.

Worked on anomaly detection and unbalanced datasets previously.

Good understanding of machine learning systems and machine learning pipelines.