WBG Pioneer - Data Analysis Internnew

WBG Pioneer Background/Organizational Context The World Bank Group’s Women, Business and the Law (WBL) project examines laws and regulations affecting women’s prospects as entrepreneurs and employees across 190 economies. Its goal is to inform policy discussions on how to remove legal restrictions on women and promote research on how to improve women’s economic inclusion. The Women, Business and the Law index serves as a road map for reform by capturing three interdependent pillars that together shape women’s access to jobs: laws, policies and institutions, and enforcement perceptions across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. Duties and Responsibilities Data Preparation, Cleaning, and Quality Control: • Clean, validate, and reconcile large, multi-source datasets, identifying and correcting errors in data values, structures, and formulas. • Develop and apply systematic processes to standardize, map, and reconcile data across teams, systems, and file formats, while using automated checks to flag inconsistencies for review. • Prepare API-ready data outputs, including JSON files, for data feeds, website visualizations, and other digital applications. • Develop and implement data-cleaning protocols, validation rules, and automated quality checks. Conduct data audits and produce monitoring and quality-assurance reports to identify issues and track their resolution. Data Analysis: • Analyze data to identify trends, patterns, and actionable insights, and prepare analytical, progress, and monitoring reports using tools such as Excel or Python. • Present findings clearly to technical and non-technical audiences through reports, presentations, and other communication products. AI Integration: • Pilot AI-powered workflow to enhance data quality, integrity, and operational efficiency. • Conduct AI-driven analysis to support data validation and insights generation. • Establish monitoring and evaluation mechanisms to track AI model performance and ensure continuous improvement. • Ensure transparency and documentation of AI workflows, including prompt engineering, versioning, and reproducibility. • Suggest ways to automate verification of survey responses with primary data sources, check survey responses against each other and follow up with respondents until a final, verified country profile is produced. • Identify and mitigate risks related to bias, accuracy, and reliability of AI-generated outputs. Data Visualization: • Interpret complex datasets and develop clear, intuitive visualization concepts that communicate key findings to technical and non-technical audiences. • Develop prototypes, mock-ups, and data visualization products for dashboards, websites, reports, and other digital applications, using tools such as Tableau, Flourish, Power BI, or similar platforms to create compelling, user-friendly visualizations.