Berkeley Public Interest Technology Clinic
Projects That Matter
Address real problems faced by public agencies and nonprofits and develop analyses, designs, tools, and systems that can make a real difference.
Master Crucial Skills
Practice scoping ambiguous problems, communicating results clearly, and translating technical insights into decisions — skills that define high-level roles in data science, consulting, and government.
What We Are
The Public Interest Technology Clinic gives graduate students the chance to do meaningful, high-impact analytical work for real public agencies and nonprofits. Students apply data science, economics, and machine learning to problems that matter — helping families access federal benefits, supporting affordable housing programs, improving city services, and strengthening pathways for underrepresented communities. You’ll work directly with government and mission-driven partners, gaining experience that few classrooms or internships provide.
Why Join
Work on real problems with real stakes. Your analysis will inform how agencies deliver services, design programs, and allocate resources.
Build a portfolio that actually matters. Every project produces a concrete product — a prediction tool, evaluation, causal analysis, or policy brief — that you can show future employers.
Learn how to work with non-academic clients. You’ll practice scoping ambiguous problems, communicating results clearly, and translating technical insights into decisions — skills that define high-level roles in data science, consulting, and government.
Be part of a growing field. Public-interest technology is one of the fastest-emerging career paths at the intersection of tech, policy, and ethics. The Clinic is a direct on-ramp.
Join the Clinic
Admission to the Public Interest Technology Clinic course is by application only.
What You’ll Get Out of It
- Hands-on experience solving open-ended, high-impact problems
- A polished work product and portfolio piece
- Direct communication and collaboration with external partners
- Training in scoping, modeling, causal inference, and ethical analysis
- A community of peers across policy, data science, and the I School
- Preparation for careers in government, civic tech, consulting, data science, or academia
Who Should Apply
Students who are curious, mission-driven, comfortable with ambiguity, and excited to use data to improve public systems. You might have a background in data science, public policy, economics, statistics, CS, or social science research — but no single background is required. Drive, professionalism, and a willingness to learn matter most.
Recent Student Projects
U.S. Treasury Department
Students built a machine-learning model to identify which families are at risk of missing the American Opportunity Tax Credit — helping Treasury target outreach and reduce unclaimed benefits.
Student takeaways: applied ML in a federal environment, model validation, communicating technical results to economists and policy staff, and understanding how data informs nationwide decisions.
Habitat for Humanity (Philadelphia)
Students conducted a causal analysis linking critical home-repair investments to client economic outcomes, supporting Habitat’s strategy and fundraising.
Student takeaways: quasi-experimental methods, cleaning and analyzing administrative data, designing a defensible research strategy, and presenting findings to nonprofit leadership.
Management Leadership for Tomorrow (MLT)
Students developed an admissions-prediction model to help MLT guide MBA applicants from underrepresented backgrounds.
Student takeaways: model development, fairness considerations, understanding the interaction between socioeconomic and academic variables, and working through ambiguous client constraints.