Computational Social Scientist

Overview

Reporting to the Associate Director, Computational Methods and Data within the Department of Data and Research Innovation, the Computational Social Scientist leads the library’s support for computationally enabled social science research. The role includes providing research consultations, designing and delivering instruction to students, faculty, and staff, and developing research workflows, analytical approaches, and technology solutions in response to continual assessment of researcher needs and identification of emerging trends in computational social science.

The incumbent will participate in the development of a research and learning service model for data‑intensive and computational social science research across disciplines, grounded in current research practices and aligned with Yale Library strategies. The role requires collaboration with other members of Data and Research Innovation to provide research support across the research data lifecycle and to develop innovative researcher and practitioner support programs, including novel approaches to research consultations, methodological community‑building, research data support, reproducible research practices, responsible use of sensitive data, specialized instruction and outreach, and other research support activities.

Activities are offered across multiple library locations, primarily at the Franke Family Digital Humanities Lab, Rosenkranz Hall, and Marx Science and Social Science Library.

• Demonstrated understanding of computational approaches to social science research, linking research questions to appropriate data and methods.
• Proficiency with advanced quantitative data analysis, methodology, and related software such as R, Python, Stata, etc., in an academic setting.
• Ability to learn new methods and tools quickly, with reference to scholarly literature and accepted practice.
• Strong written and verbal communication skills, including conveying technical and methodological subjects to non‑technical audiences, and experience offering instruction on a technical or methodological subject.
• Experience collaborating within and across organizations, knowledge of relational databases, and proficiency with survey design tools such as Qualtrics or Survey Monkey.

• Experience with high‑performance computing (HPC) and cloud compute (AWS, Azure, etc.) and experimental design, statistical analysis, and quantitative data analysis.
• Experience with data visualization tools/languages (Tableau, leaflet, D3), AI coding tools or agentic AI to support research code, strong understanding of metadata standards, and demonstrated experience writing code.
• Knowledge and experience with GIS and/or data management, and development of asynchronous learning materials such as tutorials, video tutorials, or written guides.
• Experience working with restricted or sensitive data (e.g., human subjects data) and familiarity with research ethics, privacy, and governance considerations.
• Experience providing instruction or research support in an academic research library setting.

• Conduct targeted outreach to faculty, students, and research staff to identify emerging needs in computational social science and connect them with relevant resources, datasets, and methods.
• Provide expert guidance on computational social science methods and workflows, including research design, data acquisition, data management, and analysis for social scientific questions.
• Advise researchers on reproducible and responsible research practices, including documentation, version control, privacy‑aware workflows, and appropriate use of sensitive or human‑subjects data in consultation with Yale’s HRPP.
• Design and deliver workshops, tutorials, and instructional sessions on computational social science topics.
• Create accessible learning materials such as written guides, tutorials, and interactive modules for independent learning.
• Contribute to the development and documentation of research support infrastructure, including shared analytic workflows, templates, and guidance for managing and analyzing social science data in…