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The Applied Data Fellowship 2027 (ADF) at the University of Chicago’s Harris School of Public Policy is now accepting applications on a rolling basis for its upcoming cohort. The one-year fellowship places talented graduates and early-career professionals inside government agencies, nonprofit organizations, foundations, and social impact institutions across the United States.
The program is designed for individuals who want to apply data science, analytics, artificial intelligence, project management, and policy skills to real-world challenges. Fellows work full-time with partner organizations while receiving mentorship, technical training, and professional development support throughout the fellowship year.
WhatsApp ChannelAbout Applied Data Fellowship 2027
The Applied Data Fellowship is a professional fellowship program operated by the University of Chicago. It focuses on bridging the gap between data analysis and policy implementation by embedding fellows directly within organizations that use data to improve public services, community programs, research, and decision-making.
The fellowship recruits candidates from graduate programs and professional backgrounds in fields such as:
- Data Science
- Public Policy
- Economics
- Statistics
- Computer Science
- Social Sciences
- Program Management
- Artificial Intelligence and Machine Learning
Fellows spend one year working on mission-driven projects that help organizations translate data into practical solutions and measurable outcomes.
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Applied Data Fellowship Benefits
Selected fellows receive:
- A competitive fellowship stipend
- Health insurance benefits
- One-on-one mentorship
- Weekly upskilling workshops
- Career coaching and professional development
- Technical training in data tools and policy applications
- Access to industry leaders through fireside chats and networking sessions
- Exposure to emerging AI and machine learning technologies
According to the program, fellows are supported by the University of Chicago’s technical and professional network throughout their placement.
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Fellowship Areas and Technical Skills
The program welcomes applicants with skills in:
- Python
- R
- SQL
- Tableau
- Power BI
- Machine Learning
- Artificial Intelligence
- AI Operations
- Data Engineering
- Data Infrastructure
- Geographic Information Systems (GIS)
- Project Management
Fellows may work as Data Scientists, Data Analysts, Program Managers, Data Engineers, AI Specialists, Policy Analysts, or cross-functional project leaders, depending on partner needs.
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Where Do Applied Data Fellows Work?
Fellows are placed within organizations across several sectors, including:
Government and Public Sector
Fellows support policy implementation, public service delivery, performance measurement, and data-driven decision-making.
Nonprofit Organizations
Projects often focus on education, health, social services, criminal justice, economic development, and community programs.
Research and Academic Institutions
Fellows help organizations analyze data, evaluate programs, and improve research operations.
Technology and Social Impact Organizations
Some fellows work on AI systems, analytics platforms, automation tools, and data infrastructure projects.
Past fellowship projects have included public health analytics, renewable energy initiatives, community development finance, civil rights enforcement systems, education research, and health equity programs.
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Applied Data Fellowship Eligibility
The fellowship is generally open to graduates and professionals with strong analytical and technical skills.
Applicants should demonstrate:
- Experience working with data
- Knowledge of policy, social impact, or program implementation
- Strong problem-solving abilities
- Interest in mission-driven work
- Ability to work full-time for one year
The program notes that applicants must have authorization to work in the United States. International applicants may be eligible if they can use approved work authorization pathways such as OPT or Academic Training for the full fellowship duration.
How the Selection Process Works
The Applied Data Fellowship uses a rolling admissions model.
Step 1: Submit an Application
Candidates complete an online application and provide information about their background, technical skills, experience, and interests.
Step 2: Profile Review
Applications are reviewed continuously. Candidates are typically invited to interviews when their skills align with active partner projects.
Step 3: Interview Process
Selected applicants participate in technical and behavioral interviews. The interview process usually takes about two weeks once initiated.
Step 4: Matching Phase
Successful candidates enter a matching process with partner organizations. This stage generally takes six to eight weeks.
Step 5: Fellowship Placement
Once matched, fellows begin their one-year full-time placement.
Fellowship Duration
The Applied Data Fellowship is a full-time, 12-month program. Fellows are embedded within partner organizations and work as staff-equivalent team members while receiving ongoing support from the fellowship program.
Is the Applied Data Fellowship Paid?
Yes. The Applied Data Fellowship is a paid opportunity.
The program confirms that fellows receive a competitive stipend and benefits package, including health insurance coverage after placement begins. Previous University of Chicago fellowship information indicated a stipend of approximately $47,000 per year, with adjustments possible for higher-cost locations.
How to Apply
Applications are currently open and reviewed throughout the year. Because the program operates on a rolling basis, candidates are encouraged to submit applications early to maximize matching opportunities with partner organizations.
Conclusion
The Applied Data Fellowship offers a unique opportunity for graduates and professionals to gain hands-on experience in data science, analytics, artificial intelligence, and public policy. Through one-year placements with mission-driven organizations, fellows contribute to projects that improve decision-making, public services, and community outcomes while building valuable technical and leadership skills for future careers.
