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 productive processing within modern 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