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About the role
We are looking for a highly analytical and detail-oriented Data Analyst / Senior Data Analyst to drive credit risk analytics, scorecard development, and data-driven decision-making. The ideal candidate should have strong expertise in SQL, Python/PySpark, data preprocessing, and analytics, with the ability to work on large datasets and generate actionable business insights. This role will closely collaborate with business, risk, and analytics teams to build scalable analytical solutions and improve data quality, reporting efficiency, and decision-making frameworks. The candidate will have responsibilities across the following functions: Credit Risk and Analytics: Develop and enhance credit risk scorecards and analytical strategies used during the application stage.Analyse customer and transactional data to generate actionable insights for risk and business teams.Support risk strategy development through data-driven analysis and reporting. Data Processing and Feature Engineering: Perform data cleansing, validation, merging, and enrichment to ensure high-quality datasets.Build and engineer meaningful features from raw datasets to improve analytics outcomes.Handle large-scale structured and unstructured datasets efficiently. Reporting and Automation: Develop automated analytical workflows and reporting mechanisms.Ensure data consistency, accuracy, and timeliness across reporting systems.Optimise report generation and reduce manual intervention through automation. Governance and Compliance: Ensure adherence to data governance, regulatory, and compliance standards.Maintain transparency and accuracy in analytical models and reporting outputs. Requirements: 2+ years of experience in analytics, scorecard development, or credit risk analytics.Strong expertise in SQL, including joins, aggregations, and advanced data handling techniques.Hands-on experience with Python and/or PySpark for analytics and data processing.Strong understanding of data preprocessing, feature engineering, and analytical workflows.Ability to communicate complex analytical findings to business and non-technical stakeholders.Strong problem-solving and stakeholder management skills.Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Finance, or related fields. Preferred Skills: Experience in BFSI, lending, or credit risk analytics domains.Exposure to large-scale data environments and automation frameworks.Understanding of statistical modelling and reporting best practices. Expectations: Accuracy and reliability of analytical outputs and reports.Improvement in business impact through analytics-driven decisions.Automation and optimisation of reporting workflows.Faster turnaround time for analytics and reporting delivery.Data consistency, completeness, and refresh frequency.Stakeholder adoption and satisfaction with analytics solutions We are looking for a highly analytical and detail-oriented Data Analyst / Senior Data Analyst to drive credit risk analytics, scorecard development, and data-driven decision-making. The ideal candidate should have strong expertise in SQL, Python/PySpark, data preprocessing, and analytics, with the ability to work on large datasets and generate actionable business insights. This role will closely collaborate with business, risk, and analytics teams to build scalable analytical solutions and improve data quality, reporting efficiency, and decision-making frameworks. The candidate will have responsibilities across the following functions: Credit Risk and Analytics: Develop and enhance credit risk scorecards and analytical strategies used during the application stage.Analyse customer and transactional data to generate actionable insights for risk and business teams.Support risk strategy development through data-driven analysis and reporting. Data Processing and Feature Engineering: Perform data cleansing, validation, merging, and enrichment to ensure high-quality datasets.Build and engineer meaningful features from raw datasets to improve analytics outcomes.Handle large-scale structured and unstructured datasets efficiently. Reporting and Automation: Develop automated analytical workflows and reporting mechanisms.Ensure data consistency, accuracy, and timeliness across reporting systems.Optimise report generation and reduce manual intervention through automation. Governance and Compliance: Ensure adherence to data governance, regulatory, and compliance standards.Maintain transparency and accuracy in analytical models and reporting outputs. Requirements: 2+ years of experience in analytics, scorecard development, or credit risk analytics.Strong expertise in SQL, including joins, aggregations, and advanced data handling techniques.Hands-on experience with Python and/or PySpark for analytics and data processing.Strong understanding of data preprocessing, feature engineering, and analytical workflows.Ability to
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