Senior Data Scientist

Location: Pittsburgh, PA

Are you passionate about data science and social impact? We are currently searching for a Senior Data Scientist with experience working on real-world problems, and a passion for social impact. Senior Data Scientists will guide educational/training programs, applied research projects with government agencies and non-profits in education, public health, criminal justice, environment, economic development and international development, and 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.

Ideal candidates will have 5+ years of industry experience (outside classrooms) building and deploying data-intensive python systems in production, care about the world, and like to work with other people with different backgrounds.

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. Senior Data Scientists in the group will participate in one or more of projects, such as:

  1. Improving public health in Mexico by identifying children at risk of missing important vaccinations for targeted outreach campaigns, in partnership with Fundación Carlos Slim;
  2. 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 in issue areas they care about;
  3. Identifying and connecting people in Allegheny County who are eligible but not enrolled in the Supplemental Nutrition Assistance Program (SNAP);
  4. 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.


  • Provide technical guidance in applied projects, similar to those described above 
  • Coordinating with government and non-profit partners, including scoping, ongoing updates, field trials & evaluation, and eventual implementation & deployment
  • Contributing to open source tools for building, evaluating, and mitigating disparities in machine learning models built for social good applications
  • Conducting educational programs, including courses at CMU and/or our summer Data Science for Social Good Fellowship
  • 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
  • Mentoring students and research associates who work with the group


  • 5+ years of industry/government experience required working on real-world problems 
  • Bachelor’s (or equivalent) degree required in a computational, statistical, or quantitative discipline such as computer science, machine learning, statistics, engineering, or quantitative natural or social sciences
  • Experience working with technical and non-technical project stakeholders to scope, formulate, deploy, and maintain data science systems
  • Strong Python experience
  • Experienced in using databases
  • Expertise in data analysis and machine learning using python, especially using modules such as statsmodels, scikit-learn, pandas, sqlalchemy, etc.
  • Passion for making a social impact and working with governments and non-profits

Carnegie Mellon University offers full-time employees a wide range of benefits, including health, dental, vision, transit, and retirement, as well as parental leave and tuition benefits. You can learn more here.