E T Consultant (Data Analyst)

World Bank Group•Washington, DC

1d•Onsite

About The Position

The Information and Technology Solutions (ITS) Vice Presidential Unit (VPU) enables the World Bank Group to achieve its mission of ending extreme poverty and boost shared prosperity on a livable planet by delivering transformative information and technologies to its staff working in over 150+ locations. The Data Analyst is responsible for analyzing data assets to support organizational decision-making. With guidance, this role assists in the design and execution of data analytics initiatives, supports collaboration with stakeholders, and supports the development of statistical models, predictive models and machine learning techniques to solve complex business problems.

Requirements

Bachelor’s or master’s degree with 2 years of experience or equivalent combination of education and experience.
Business Intelligence (Skilled): Designs, develops, and maintains BI solutions including dashboards, reports, and analytical tools. Translates business requirements into technical specifications and delivers actionable insights to support decision-making across the organization.
Data Visualization (Skilled): Builds clear, compelling, and interactive visual representations of complex datasets using tools such as Power BI, Tableau, or similar platforms. Tailors visualizations to audiences need to effectively communicate trends and insights.
Data Modeling (Skilled): Designs and implements conceptual, logical, and physical data models. Ensures data structures are optimized for performance, scalability, and alignment with business requirements.
Data Science (Skilled): Applies statistical methods, machine learning techniques, and analytical frameworks to extract meaningful patterns from large datasets. Translates analytical findings into practical recommendations for business stakeholders.
Machine Learning (Skilled): Develops, trains, and evaluates machine learning models for classification, regression, clustering, and prediction tasks. Familiar with model lifecycle management including validation, deployment, and monitoring.
Programming / Coding (Skilled): Hands on in one or more programming languages such as Python, R, SQL, or similar. Writes clean, maintainable, and well-documented code to support data pipelines, analytics workflows, and application development.
Agile Methodologies (Skilled): Works effectively within Agile frameworks including Scrum and Kanban. Participate actively in sprint planning, retrospectives, and iterative delivery cycles to ensure timely and high-quality outputs.
Artificial Intelligence (AI) (Skilled): Understands and applies AI concepts and tools including natural language processing, computer vision, and generative AI. Evaluates AI solutions for fit, ethical use, and organizational impact.
Business Intelligence (Awareness): Understands the purpose and value of BI tools and reporting platforms. Able to consume and interpret BI outputs and contribute to requirements gathering for BI initiatives.
Data Warehousing (Awareness): Familiar with data warehouse concepts including ETL processes, dimensional modeling, and data lake architectures. Understands how data warehouses support enterprise reporting and analytics needs.
Data Governance (Awareness): Familiar with data governance principles including data quality, data stewardship, metadata management, and compliance. Supports governance initiatives by adhering to established policies and standards.
Business Requirements Analysis (Skilled): Elicits, documents, and validates business requirements through stakeholder engagement, workshops, and structured interviews. Translates business needs into clear functional and technical specifications, ensuring alignment between business objectives and delivered solutions.
Agile Methodologies (Skilled): Works effectively within Agile frameworks including Scrum and Kanban. Participates actively in sprint planning, retrospectives, and iterative delivery cycles to ensure timely and high-quality outputs. Experienced in supporting Scrum Master responsibilities including facilitating daily stand-ups, removing impediments, and fostering a culture of continuous improvement within the team.
Analytical Reasoning (Skilled): Synthesizes complex information from multiple sources to draw well-reasoned conclusions. Applies structured thinking to break down ambiguous problems and develop evidence-based solutions.
Problem-Solving (Skilled): Identifies root causes of complex challenges and develops practical, innovative solutions. Proactively addresses obstacles and adapts approaches based on evolving information and constraints.
Critical Thinking (Skilled): Evaluates information objectively, questions assumptions, and assesses the validity of data and conclusions. Applies logical reasoning to make sound judgments in ambiguous or high-stakes situations.

Nice To Haves

Microsoft Certified: Power BI Data Analyst Associate
SAFe Practitioner
Databricks Certified Data Analyst Associate

Responsibilities

Supports analytical services or operational work in core areas of data science.
Assists with the development of a data governance framework that includes clear guidelines for data classification, ownership, and stewardship.
Leverages statistical concepts, practice, and theory, and knowledge of probabilities to support stakeholders in identifying key operational issues and optional solutions.
Supports the delivery of data, research and evidence-based solutions for institutional directions and strategic vision.
Collaborates to develop data solutions and market knowledge and ensure continuous processes improvements.
Assists with the development of data models and manages data processing and cleaning, supporting quality assurance and data visualization.
Supports the development of machine learning and AI tools to accelerate reasoning and analysis to support decision making.
Collects and documents business requirements, including establishing acceptance and evaluation criteria.
Defines configuration specifications and business requirements to ensure alignment with roadmap objectives.
Helps teams self-organize, self-manage, and deliver via effective SAFe Agile methods.
Assist in the preparation process for PI planning, and drafts PI plans in coordination with the Agile team.
Tracks team progress and utilize metrics to identify areas for enhancement.
Help translate technical analyses in core areas of data science into recommendations for programs, policies, and strategies for social and economic development.
Supports the adoption and suggestion of new concepts and ideas to from inside and outside the organization to optimize data solutions.