Applied Data Fellowship 2026 at University of Chicago Harris School of Public Policy (Paid, 12 Months)

The Applied Data Fellowship is a highly selective, 12 month, full time program run by the University of Chicago Harris School of Public Policy. It embeds early career professionals who have strong data skills into local government bodies and mission driven nonprofit organisations across the United States. If you want to turn analysis into real policy decisions, new programs, and operational change, this fellowship is built to bridge the gap between data work and measurable public impact.

Applications are reviewed on a rolling basis and the window stays open year round, so there is no single hard cutoff. The program has placed more than 200 fellows and worked with over 100 organisations to date.

The Harris School of Public Policy is one of the leading public policy schools in the world and is part of the University of Chicago. The Applied Data Fellowship is its flagship effort to connect data talent with public interest organisations. Fellows gain access to the University of Chicago network and ecosystem, professional development, technical training, and year long mentorship from experienced leaders. The program is competitive: fellows are selected from a national pool reported at more than 1,200 applicants.

As a fellow you receive a structured 12 month placement plus a strong support system around it. Confirmed benefits include:

• A $47,000 living stipend for the year, scaled up for placements in cities with a higher cost of living than Chicago
• A separate reimbursable health insurance stipend of up to $5,000
• One on one mentorship throughout the year
• Weekly upskilling workshops
• Access to the UChicago Safety Net for technical guidance and oversight
• Portfolio building work on real, high stakes projects
• Access to the University of Chicago professional network

Note that the stipend and health insurance reimbursement begin only after you are successfully matched and placed with a partner organisation.

Fellows work across the full data pipeline, from identifying a problem through analysis to implementation and organisational adoption. Typical projects include:

• Data infrastructure: building data warehouses, setting up governance systems, standardising metrics
• Analytics and visualisation: dashboards in Tableau and Power BI, automated reporting
• AI and machine learning: predictive models and AI powered platforms
• Process improvement: intake systems, workflow automation, cleaning data at scale
• Policy translation: turning insights into actionable recommendations, programs, and operational changes

Past placements span public health (Cook County Health), clean energy policy (Illinois Power Agency), community finance (Opportunity Finance Network, National Community Investment Fund), criminal justice reform (Cook County Justice Advisory Council), civil rights enforcement, education research (CASEL), and scientific publishing (American Association for the Advancement of Science).

The program looks for graduate level candidates from fields including public policy, data science, economics, social sciences, computer science, and related areas. It is primarily intended for those graduating from their academic programs in 2026 and/or already working full time. You should be able to demonstrate:

• A solid understanding of public policy
• Strong data analysis skills
• Project management ability
• A drive to implement programs across government, nonprofit, and social enterprise settings

Candidates come from a wide variety of academic and professional backgrounds, so the path you took matters less than the skills you can show.

You should be comfortable with practical technical skills in one or more of the following areas:

• Python
• R
• SQL
• Machine learning
• Program management

The fellowship is a full time, one year commitment based on placement in the United States, so you should be able to take on a year long role.

The fellowship moves through three phases. In Scope and Embed, partner organisations identify priorities and fellows are selected for 12 month placements. In Learn and Contribute, fellows take on analysis, visualisation, data engineering, machine learning deployment, and policy translation. In Co Manage and Succeed, fellows deliver results to partners while receiving mentorship and oversight.

The hiring flow runs on a rolling timeline: the application window is open year round, interviews are expected to begin in March, the interview process takes about two weeks once you are invited, and the time from offer to placement is roughly six to eight weeks. One important matching rule: if you are not matched with a project within three months of entering the finalist pool, you are not continued in the program.

Applications are submitted through a short online form that takes about ten minutes to complete. Start at the official Apply page and follow the link to the application form. Because review is rolling and year round, you can apply at any time, and earlier applications enter the pipeline sooner.

• Confirm you can commit to a full time, 12 month placement in the United States
• Make sure your background fits graduate level public policy, data science, economics, social sciences, computer science, or a related field
• Be ready to show practical skills in Python, R, SQL, machine learning, or program management
• Prepare to describe your project management experience and public policy understanding
• Set aside about ten minutes to complete the online application form
• Submit early, since review is rolling and placements fill on an ongoing basis