Computational Social Scientist

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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. This includes providing research consultations and designing and delivering instruction to students, faculty, and staff. The Computational Social Scientist may also develop and support 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 based on current research practices, grounded in student and faculty academic needs, and aligned to Yale Library strategies. The incumbent will collaborate with other members of Data and Research Innovation to provide research support across the research data lifecycle and contribute to the development of 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, but primarily occur at the Franke Family Digital Humanities Lab, Rosenkranz Hall, and Marx Science and Social Science Library.

Yale Library serves a diverse staff, campus, local, and global community and is deeply committed to advancing its goals for equity, inclusion, and accessibility. This commitment is reflected in our mission, strategic directions, staff values, initiatives, and scholarly activity. To support our goals for staff recruitment, retention, and development, Yale Library invites applications from candidates whose philosophies align with these commitments and who have demonstrated success with supporting diversity and inclusion through their work, service, research, and/or teaching.

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

• Experience with using or supporting high-performance computing (HPC) and cloud compute (AWS, Azure, etc.). Experience with experimental design, statistical analysis, and quantitative data analysis.
• Experience with data visualization tools/languages (Tableau, leaflet, D3). Experience using or advising on the use of AI coding tools or agentic AI to support research code. Strong understanding of metadata standards and demonstrated experience writing code.
• Knowledge of and experience with GIS and/or data management. Demonstrated experience developing asynchronous learning materials such as tutorials, video tutorials, or written guides.
• Demonstrated experience working with restricted or sensitive data (eg. human subjects data) and/or familiarity with research ethics, privacy, and governance considerations.
• Demonstrated 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.
• In collaboration with other members of Data and Research Innovation, advise researchers on reproducible and responsible research practices, including documentation, version…