Computer Science . Machine Learning

USA Computer Science Machine Learning Others

About this opportunity

The Applied Data Fellowship (ADF) at the University of Chicago Harris School of Public Policy is a high-impact program that embeds data professionals into mission-driven organizations. Fellows serve as “Analysts,” “Builders,” or “Data Shamans,” working for one year to move critical projects from initial scoping to full-scale implementation within government agencies, nonprofits, and foundations.

The fellowship is designed for individuals who possess elite analytical skills and a deep commitment to social impact. Fellows are not just “crunching numbers”; they are coordinating stakeholders, building dashboards, and implementing AI/ML solutions that drive systemic change. The program provides a “Safety Net” of technical mentorship from the UChicago ecosystem, ensuring that every fellow has the support needed to overcome complex implementation hurdles.

The selection process is highly competitive, with a focus on matching specific technical profiles with active partner projects. Beyond the work placement, the ADF offers a year of continuous learning through weekly upskilling workshops, fireside chats with industry leaders, and regular demos of cutting-edge data tools. This combination of hands‑on experience and structured learning makes the ADF a premier launchpad for careers at the intersection of data science and public policy.

Target Audience:
Graduate‑level analysts, builders, and problem solvers from programs in public policy, data science, economics, computer science, and related fields.

Geographic Eligibility:
United States (various locations based on partner placements).Tangible Benefits:
Competitive stipend, comprehensive health insurance, one‑on‑one mentorship, weekly upskilling workshops, and access to the UChicago technical ecosystem.

Final date to receive applications:
Rolling (Applications are reviewed year-round).Program Duration: 1 year (Full‑time commitment).