Credit Risk Data Analyst –SQL, Python & PySpark

Experience: 3–7 Years

Location: Bangalore

Notice - Immediate to 15 Days

Role Summary

Seeking a Credit Risk Data Analyst with strong hands-on expertise in Python and PySpark to support large-scale data processing, validation, and analytics across credit risk datasets. The role focuses on leveraging distributed data frameworks to ensure data accuracy, scalability, and efficient processing within up-to-date data platforms.

Key Responsibilities

- Perform large-scale data processing, transformation, and validation using Python and PySpark
- Build and optimize data pipelines for ingestion, processing, and analysis of credit risk datasets
- Conduct exploratory data analysis (EDA), anomaly detection, and trend identification
- Implement data quality checks covering accuracy, completeness, and consistency
- Perform data reconciliation and support data migration validation activities
- Work with distributed data environments (e.g., big data platforms)



to handle high-volume datasets
- Collaborate with stakeholders to understand business requirements and data flows
- Assist in preparing and validating datasets used in PD, LGD, and EAD models
- Partner with data engineering and risk teams to improve data processing efficiency

- Required Skills

- Strong hands-on experience in Python and PySpark
- Experience in building and optimizing data pipelines in distributed environments
- Solid understanding of data transformation, validation, and big data processing
- Exposure to data quality frameworks and governance practices

Domain Requirements (Uniform Across JDs)

- Strong understanding of Credit Risk fundamentals
- Exposure to PD (Probability of Default), LGD (Loss Given Default), and EAD (Exposure at Default) models
- Knowledge of retail banking portfolios such as loans, cards, and mortgages

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