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About This Role
WBG Pioneer \- Data Scientist Intern
Job \#: req37616
Organization: World Bank
Grade: T4 (No\-fee)
Location: Washington, DC,United States
Hiring Manager: Marcelo Donolo
Required Language(s): English
Preferred Language(s):
Closing Date: 8/12/2026 (11:59pm UTC)
Description
WBG Pioneers, the World Bank Group’s Internship Program, offers undergraduate and postgraduate students a high impact learning experience at the heart of global development. Participants gain hands on experience in a diverse and dynamic environment, contribute fresh perspectives and innovative ideas, and connect with international professionals working to end poverty on a livable planet.
The Integrity Vice Presidency (INT) investigates and helps prevent fraud and corruption in World Bank Group–financed projects. Within INT, the Data Lab is a multidisciplinary team—spanning data, AI, analytics, and cloud engineering—that builds and operates production\-grade, responsible data and AI solutions to enable and streamline INT's core business processes.
This 150\-day internship offers a hands\-on opportunity to work at the frontier of enterprise data intelligence. Under the direct supervision and mentorship of the Data Lab Team Lead, the Data Scientist Intern will learn to design and build data solutions and data\-driven agents that support INT's core functions. The intern will also be guided by the Data Officer and Data Engineer to support data reporting tasks. The role is well suited to a fast learner who stays aligned with industry trends in enterprise data intelligence and is eager to apply modern analytics and AI techniques to real\-world integrity use cases within a secure, governed cloud environment. Duties and Responsibilities* Learn and apply the Data Lab's data practices to help design and build data solutions and data\-driven agents that streamline INT business processes.
- Support periodic and ad\-hoc reporting to inform business decision\-making and strategic planning.
- Analyze large datasets to extract insights and identify trends, patterns, and anomalies.
- Build and maintain interactive dashboards and visualizations (e.g., Power BI, Tableau) to present data findings.
- Assist in developing and optimizing data pipelines and queries (e.g., on Databricks and SQL) for performance and reliability.
- Conduct data quality assurance (QA) to ensure the accuracy, completeness, and reliability of data.
- Collaborate with stakeholders and Data Lab members to gather and refine data requirements for projects and reports.
- Explore and evaluate emerging enterprise data intelligence tools and trends, sharing findings and recommendations with the team.
- Maintain clear, up\-to\-date documentation of data sources, transformations, and reporting processes.
- Participate in team meetings, provide regular progress updates, and present work to the Data Lab Team Lead.
Selection Criteria
- Currently enrolled in, or in the final year of, an undergraduate or postgraduate (master's/PhD) program in data science, statistics, computer science, analytics, engineering, or a related field.
- 0–2 years of relevant professional experience; academic, research, or project experience in data analytics/data science is required. Hands\-on experience with Python and SQL for data manipulation and analysis.
- Experience with Databricks, Power BI, and AI/machine learning is a strong asset.
- A fast learner who actively follows industry trends in enterprise data intelligence and can quickly apply new techniques in a hands\-on manner.
- Strong analytical, research, and problem\-solving skills, with a self\-motivated and detail\-oriented approach. Effective written and verbal communication skills, including the ability to document and present technical work clearly.
- Ability to work effectively in a diverse, team\-based environment and manage multiple priorities within tight deadlines.
- Demonstrated interest in development work and the World Bank Group's mission.
Note
No\-Fee Internship Eligibility: This position is offered under the WBG Pioneers No\-Fee Internship Track. Students may be offered a no\-fee STT appointment provided that they either: (a) are enrolled in a Master's, PhD, or similar graduate program during the entire internship (or are in the fifth year or higher of a degree program in countries where higher education is not divided into undergraduate and graduate stages) and provide an official letter from their university confirming that the internship fulfills academic requirements for at least one term of study; or (b) are enrolled in undergraduate or graduate studies and receive a stipend from their university at least equivalent to the minimum STT T1 fee level in effect at the start of the assignment, as confirmed by an official university letter.
WBG Culture Attributes:
1\. Sense of urgency: Anticipate and quickly respond to the needs of internal and external stakeholders.
2\. Thoughtful risk\-taking: Challenge the status quo and push boundaries to achieve greater impact.
3\. Empowerment and accountability: Empower yourself and others to act and hold each other accountable for results.
*The World Bank Group values diversity and encourages all qualified candidates who are nationals of World Bank Group member countries to apply, regardless of gender, gender identity, religion, race, ethnicity, sexual orientation, or disability. Sub\-Saharan African nationals, Caribbean nationals, and female candidates are strongly encouraged to apply.*
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% 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 $192,890 based on 463 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
World Bank Group AI Hiring
World Bank Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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