Job Description
Credit Risk Data Analyst –SQL, Python & PySpark
nExperience: 3–7 Years
nLocation: Bangalore
nNotice - Immediate to 15 Days
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nRole Summary
nSeeking 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.
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nKey Responsibilities
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- Perform large-scale data processing, transformation, and validation using Python and PySparkn
- Build and optimize data pipelines for ingestion, processing, and analysis of credit risk datasetsn
- Conduct exploratory data analysis (EDA), anomaly detection, and trend identificationn
- Implement data quality checks covering accuracy, completeness, and consistencyn
- Perform data reconciliation and support data migration validation activitiesn
- Work with distributed data environments (e.g., big data platforms)



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

nRequired Skills
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- Strong hands-on experience in Python and PySpark n
- Experience in building and optimizing data pipelines in distributed environmentsn
- Solid understanding of data transformation, validation, and big data processingn
- Exposure to data quality frameworks and governance practicesn

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nDomain Requirements (Uniform Across JDs)
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- Strong understanding of Credit Risk fundamentals n
- Exposure to PD (Probability of Default), LGD (Loss Given Default), and EAD (Exposure at Default) modelsn
- Knowledge of retail banking portfolios such as loans, cards, and mortgagesn

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