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 contemporary 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