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Post-Doc Researcher (comp listed)

Data Science for Social Good at Carnegie Mellon University
Data Science for Social Good at Carnegie Mellon University
Job Functions:
Data Science
Employment Types:
Pittsburgh, PA, USA
55,000 – 62,000 USD per year
Posted on:

Are you passionate about helping governments and non-profits be more equitable and more effective with your AI/ML/Data Science skills? We currently have Post-Doc positions for recent PhDs in these areas and a passion for social impact. Post-Docs will support educational and training programs, applied research projects with government agencies and non-profits in education, public health, criminal justice, environment, economic development and international development, and perform research in areas such as interpretability, bias and fairness, and other machine learning methods focused on problems in social sciences and public policy with a strong emphasis on releasing the work through open source code, shared curriculum, and publications.

We are a team at Carnegie Mellon University (in Pittsburgh) across the School of Computer Science and the Heinz College of Public Policy focused on using AI, machine learning, and data science to have a positive and equitable impact on society. Our interdisciplinary group works at the intersection of research, practice, and education to improve society and public policy.

Much of our group’s work surrounds applied research projects focused on positive social impact. Post-Doctoral fellows working in the group will participate in one or more of projects, such as:

  • Improving public health in Mexico by identifying children at risk of missing important vaccinations for targeted outreach campaigns, in partnership with Fundación Carlos Slim;
  • Working with the American Civil Liberties Union (ACLU) to support their efforts to promote legislative outcomes that improve individual liberties by developing tools that help them efficiently surface bills to take action on issue areas they care about;
  • Identifying and connecting people in Allegheny County who are eligible but not enrolled in the Supplemental Nutrition Assistance Program (SNAP);
  • Research investigating the measurement and mitigation of bias and disparities in ML-assisted decision making systems in public policy contexts, including empirical studies expanding on our project work as well as the development of new tools;
  • Explorations of model interpretability systems to help improve the overall performance of human-ML systems, such as decisions about whether to implement or override specific model recommendations, mapping appropriate interventions to people with need, or debugging and refining models themselves;
  • New projects with government and non-profit organizations as they arise, spanning a wide range of policy domains, including social justice, healthcare, education, public safety, economic development, environment, and more.


The specific focus and responsibilities for each postdoctoral fellow in the group may vary with the person’s preferences and the group’s needs, but will generally include:

  • Playing a key technical role in at least one of the applied projects described above (or similar projects as they arise)
  • Coordinating with the government and non-profit partners for these projects, including scoping, ongoing updates, field trials & evaluation, and eventual implementation & deployment
  • Participating in research focused on bias and fairness in machine learning, model explainability, or methods that are tailored to the nuances of problems in public policy
  • Contributing to open source tools for building, evaluating, and mitigating disparities in machine learning models built for social good applications
  • Participating in educational programs, including courses at CMU and/or our summer Data Science for Social Good Fellowship
  • Mentoring students and research associates who work with the group


  • PhD (or equivalent) degree required, preferably in computer science, statistics, or quantitative social sciences
  • Strong Python experience
  • Experienced in using databases
  • Expertise in data analysis and machine learning using python is a plus, especially using modules such as statsmodels, scikit-learn, pandas, sqlalchemy, etc.
  • Experience working on real-world problems and passion for making a social impact

Carnegie Mellon University offers post-doctorate fellows a range of benefits, including health, dental, vision, transit, and retirement. You can learn more here: https://www.cmu.edu/hr/benefits/index.html

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