Interested in this Data Scientist role at World Bank Group?
Apply Now →Skills & Technologies
About This Role
E T Consultant - Data Scientist
Job #: req38313
Organization: World Bank
Sector: Research & Quantitative Analysis
Grade: EC2
Term Duration: 1 year 0 months
Recruitment Type: Local Recruitment
Location: Washington, DC,United States
Required Language(s): English
Preferred Language(s):
Closing Date: 9/28/2026 (MM/DD/YYYY) at 11:59pm UTC
Description
Working at the World Bank Group (WBG) provides a unique opportunity to help client countries solve their great-est development challenges. The WBG is one of the largest sources of funding and knowledge for developing countries—a unique global partnership of five institutions dedicated to ending extreme poverty, increasing shared prosperity, and promoting sustainable development on a livable planet. With more than 120 offices worldwide, we work with public and private sector partners, investing in groundbreaking projects and using data, research, and technology to develop solutions to the most urgent global challenges. For more information, visit www.worldbank.org.
Office of the Managing Director and Chief Knowledge Officer
The Managing Director and Chief Knowledge Officer (MDCKO) oversees the World Bank Group's knowledge and partnership agenda and helps ensure that the institution's analytical and operational work responds to the most pressing development challenges facing client countries and global partners. The office plays a key role in shaping the WBG's strategic direction, supporting key partnerships with shareholders, development agencies, academia, and other stakeholders, and strengthening how knowledge, evidence, and learning inform development impact. With the launch of the Knowledge Bank at the start of 2026, the WBG is deepening its focus on one of the areas client countries value most: global knowledge on what works, where, and why. The Knowledge Bank is intended to bring global expertise closer to country challenges, strengthen collaboration across sectors and institutions, and help scale practical solutions that can improve lives, create jobs, and support better development outcomes.
As part of this model, the WBG now operates through five vertical vice presidencies aligned with the mission: People, Prosperity, Planet, Infrastructure, and Digital & AI. In addition, three horizontal departments reporting to the MDCKO enhance knowledge, innovation, and outcome orientation across the WBG. One of those horizontal departments focuses on improving the institution's outcome orientation: the Department for Outcomes.
About the Department for Outcomes
The Department for Outcomes is a corporate delivery and change team at the center of the World Bank Group's shift from measuring activity to managing for impact. Its mandate is to make outcomes visible, comparable, and actionable so that leaders and teams can understand where the institution is changing lives, where progress is off track, and what needs to change next.
The Department leads the implementation of the World Bank Group Scorecard as an accountability, management, and communications tool. It develops and improves outcome indicators, methodologies, and targets in areas such as jobs, climate, private capital, gender, and youth; provides quality assurance; and connects outcome ambitions to knowledge, operations, and financing. Through its Outcome Measurement and Outcome Delivery units, the De-partment combines corporate reporting with portfolio analytics, forecasting, regular stocktakes, and action plan-ning across the World Bank, IFC, and MIGA.
The Department also has a growing global role. It convenes multilateral development banks, development agen-cies, researchers, and external experts to improve and align outcome approaches, build institutional capacity, and modernize the profession of results measurement. Its work reaches from project-level data to senior management decisions, and from new methodologies inside the World Bank Group to shared approaches across the internation-al development system.
Technology and innovation are core to how the team works. The Department develops AI-enabled analytics, data pipelines, forecasting models, interactive visualizations, and decision-support products that turn large and com-plex datasets into clear choices for operational teams, senior management, and external stakeholders. A piece of analysis is not considered finished when it is technically correct; it is finished when it helps improve a decision, a process, or a development outcome.
The culture is entrepreneurial, collaborative, and demanding. The team moves quickly because the needs are ur-gent, tests ideas early, learns from users, improves continuously, and expects high standards from concept through delivery. It values intellectual curiosity, rigorous analysis, direct communication, ownership, thoughtful risk-taking, and the readiness to challenge established practices. Young talent has been a major source of the Depart-ment's innovation and rapid delivery; the selected Data Scientist ETC will be expected not only to contribute to the agenda but to help shape it.
Position Context
The Department for Outcomes is seeking dynamic, versatile Data Scientist Extended Term Consultants (ETCs) who operate fluently across code, evidence, strategy, and executive delivery. The successful candidate will com-bine strong data science and quantitative research skills with knowledge of development operations and a passion for evidence-informed decision-making and institutional change. The position offers direct exposure to corporate strategy, high-level leadership decision-making, and the global ecosystem of multilateral development banks (MDBs).
This single selection process is designed to select and hire up to four (04) Data Scientist ETCs, subject to candi-date profile, technical screening, and interview evaluations. Selected candidates will be mapped across workstreams within the Outcome Measurement and Outcome Delivery units, based on business needs.
Selected consultants will join the team on-site at WBG Headquarters in Washington, D.C.
What You’ll Deliver
The Data Scientist ETC will contribute across analytical, technical, and operational workstreams:
- Indicator Methodology, Design & Quality Assurance: Support the development, testing, and refinement of outcome indicators and methodologies at both the project and portfolio level. This includes end-to-end data collection, data cleaning, exploratory analysis, and rigorous quality assurance of WBG Scorecard indicators, as well as conducting manual and automated reviews of results frameworks in operational project documents (e.g., Project Appraisal Documents [PADs], Implementation Status and Results Reports [ISRs], and Imple-mentation Completion and Results Reports [ICRs]) when required.
- Outcome Targets & Predictive Forecasting: Support the formulation and regular monitoring of outcome targets to inform evidence-based decisions on corporate strategies and operational portfolios. Responsibilities include in-depth reviews of strategy and project documentation, curation of outcome datasets, application of statistical and forecasting techniques to project future portfolio-level trajectories, and synthesis of strategic findings.
- AI for Outcomes Engineering & Tool Development: Actively contribute to the AI for Outcomes program by designing, building, evaluating, and maintaining analytical tools, data pipelines, and intelligent products. This includes engineering applications powered by Large Language Models (LLMs), natural language processing (NLP), and machine learning to strengthen outcomes measurement, extract operational insights from unstruc-tured text, and support evidence-based decision-making.
- Methodological Notes & Guidance Development: Author and contribute to methodological notes, indicator reference sheets, and technical guidance documents. Ensure quality assurance of results data by drawing on leading research and evaluation methods used across MDBs and international partners, directly strengthening the credibility, consistency, and comparability of reported outcomes.
- Portfolio Analytics & Strategic Executive Insights: Analyze outcome metrics from across WBG operations, aggregate portfolio results, and monitor where and how the institution is delivering on its development com-mitments. Extract actionable insights and strategic signals to feed into corporate decision-support platforms utilized by WBG Senior Leadership.
- Executive Visualization & Strategic Communication: Translate complex, multidimensional analytical and econometric findings into clear, visually compelling, and consulting-style deliverables. This includes crafting high-level executive slide decks, briefs, interactive dashboards, talking points, and briefing memos tailored for senior management, technical peers, and non-specialist audiences.
- Thematic Workstreams & Action Plans: Contribute to the rollout of outcome-related corporate action plans and cross-cutting thematic work (such as jobs, climate, private capital mobilization, women, and youth) in close collaboration with Regional operational teams, vertical Vice Presidencies, IFC, and MIGA.
- Stakeholder Convening & Global Partnerships: Support the Department’s convening, learning, and capaci-ty-building engagements with internal and external stakeholders, including working groups with other multi-lateral development banks, partner development agencies, academic experts, and external advisory bodies such as the Jobs Council.
- Workstream & Project Management: Provide proactive project management support across analytical workstreams, including scoping tasks, coordinating inputs from multidisciplinary teams, tracking milestones, managing delivery timelines, and ensuring outputs meet the highest standards of technical rigor.
- Ad-Hoc Program Delivery: Contribute flexibly to additional emerging work program priorities, rapid-response analytical requests, and corporate initiatives of the Department for Outcomes as needed.
Selection Criteria
- Minimum of a master’s degree in Data Science, Computer Science, Economics, Statistics, Applied Mathemat-ics, Information Systems, Public Policy, Public Administration, International Development, Business Man-agement, or a closely related quantitative field, ideally with a strong concentration in analytics. A Ph.D. is considered an advantage.
Professional Experience:
- Minimum of 5 years of professional experience in data science, analytics, results measurement, monitor-ing and evaluation (M&E), portfolio management, management consulting, development finance, or public policy.
- Prior experience in management consulting or portfolio management with direct exposure to senior executive audiences is a distinct advantage.
- Proven ability to bridge the gap between technical code, data pipelines, and strategic business value for insti-tutional decision-makers.
Technical & Methodological Competencies:
- Programming & Modeling: High proficiency in Python and SQL (proficiency in R and/or Stata is also an asset). Strong applied knowledge of statistical analysis, predictive modeling, machine learning, NLP, and modern LLM application workflows.
- Data Engineering & Workflow Discipline: Working knowledge of modern software engineering best prac-tices (version control with Git, modular code architecture, rigorous testing, and reproducible analytical work-flows). Experience processing structured and unstructured datasets, interacting with APIs, managing relational databases, and conducting data quality validation. Experience deploying interactive dashboards or end-to-end data pipelines is an advantage.
- Research, Indicators & Evaluation: Solid understanding of empirical research, indicator development, evi-dence sources, and evaluation methodologies, including approaches standard across MDBs and international organizations.
- Operational Literacy: Familiarity with development operations, operational project cycles (PADs, ISRs, ICRs), results measurement frameworks, or the WBG Scorecard is an advantage.
- Visualization & Communication: Advanced skills in data visualization and executive presentation (includ-ing high proficiency in Excel and PowerPoint), with a demonstrated knack for structuring complex data into clean, executive-ready visual narratives.
Behavioral & Interpersonal Competencies:
- Integrative & Strategic Thinking: Demonstrated capacity to synthesize disparate technical, operational, and qualitative inputs from across sectoral boundaries into an integrated institutional perspective.
- Client Orientation & Delivery Under Pressure: High sense of ownership, adaptability, and an outside-in mindset capable of prioritizing and delivering high-quality products under tight deadlines in a fast-paced en-vironment.
- Teamwork & Collaboration: Demonstrated success collaborating effectively in diverse, multicultural, mul-tidisciplinary teams, serving as an effective bridge between technical engineers and operational specialists.
- Language Skills: Full professional fluency in English (verbal and written) is required. Working proficiency in another official WBG language (French, Spanish, Arabic, Russian, or Chinese) is an asset.
WBG Culture Attributes:
- Sense of urgency: Anticipate and quickly respond to the needs of internal and external stakeholders.
- Thoughtful risk-taking: Challenge the status quo and push boundaries to achieve greater impact.
- Empowerment and accountability: Empower yourself and others to act and hold each other accountable for results.
World Bank Group Core Competencies
As per WBG policy, an Extended Term (ET) appointment is subject to a lifetime maximum of three (3) years. Former and current ET staff who have completed or are in the process of completing their third-year ET appointment are not eligible for future ET appointments.
We are proud to be an equal opportunity and inclusive employer with a dedicated and committed workforce, and do not discriminate based on gender, gender identity, religion, race, ethnicity, sexual orientation, or disability.
Learn more about working at the World Bank and IFC including our values and inspiring stories.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At World Bank Group, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
World Bank Group AI Hiring
World Bank Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.