Principal Data Analyst (Rural Analytics)

The core responsibilities for the job include the following:

Credit Risk and Analytics:

• Develop and enhance credit risk scorecards and analytical strategies used during the application stage.
• Analyze 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.
• Optimize 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.

Key Success Metrics:

• Accuracy and reliability of analytical outputs and reports
• Improvement in business impact through analytics-driven decisions
• Automation and optimization of reporting workflows
• Faster turnaround time for analytics and reporting delivery
• Data consistency, completeness, and refresh frequency
• Stakeholder adoption and satisfaction with analytics solutions

Requirements:

• 8+ 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 modeling and reporting best practices.

Skills

PySpark, Python, SQL, analytics, credit risk, scorecards