University of Washington - Computational Demographer

paid / Seattle / full-time

An exciting new Computational Demographer position at the University of Washington, Seattle, joint between the Center for Studies in Demography and Ecology and the eScience Institute. Put your R skills to use through work on demographic projects, mentoring Data Science for Social Good summer students, training researchers across campus in data science skills.

The Center for Studies in Demography and Ecology (CSDE; seeks a Computational Demographer to join its Scientific Core. This position will have a joint role and complementary responsibilities within the UW eScience Institute. CSDE is a population research center at the University of Washington funded by a P2C grant from the Population Dynamics Branch at the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), matching funds from the College of Arts and Sciences, the UW Provost’s Office, eScience Institute, other UW Schools and Colleges, as well as faculty affiliate grant proposals. CSDE is uniquely situated at the UW to foster cutting-edge population research, including our partnership with the Northwest Federal Statistical Research Data Center (NWFSRDC), the UW Data Collaborative, an active demographic graduate training program, and initiatives on campus linking and sharing administrative data and driving the formation of interdisciplinary teams together for scientific, evidence-based policymaking. To further foster scholastic connections, CSDE maintains active linkages with other research centers on campus, including the eScience Institute, the Center for Social Science Computation and Research (CSSCR), the Center for Statistics in the Social Sciences (CSSS), the West Coast Poverty Center, and the Population Health Initiative, among many others.

CSDE and the eScience Institute are excited to partner on this position to support the increasing demand for computational demographers across our organizations and more broadly at the UW. The mission of the eScience Institute is to empower researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data. As the hub of data-intensive discovery on campus, the Institute leads a community of innovators in the techniques, technologies, and best practices of data science and the fields that depend on them. The Institute does this by bringing expertise and helping researchers at UW to leverage data science tools, methods, and best practices in their research and in their grant proposals. As data science experts and, in collaboration with faculty and partner organizations (like CSDE), the Institute advances the state-of-the-art in data science methods and in domain sciences that benefit from them.

This position provides methodological support to faculty and graduate students studying demographic processes and their connections to population health dynamics, as well as leadership on innovative new projects consistent with the scientific emphases of CSDE and eScience. It requires broad social science research skills, advanced computational skills, and an orientation towards service and facilitation of research. The successful candidate will provide support for researchers in the areas of responsible conduct of research, transparent science, data construction, statistical and/or machine learning training, demographic forecasting and probabilistic modeling. We are especially interested in candidates with experience in the application of machine learning, natural language processing, and probabilistic linkages for relational, high-dimensional data for population science projects. Emphasis in these areas will benefit faculty using novel data sources (e.g. social media data, narratives in administrative records) to capture features of population processes generally absent or underrepresented in current data. Accordingly, this position requires exceptional interpersonal and communication skills; flexibility, adaptability and the desire to learn new skills as needed; and the ability to work as part of a team. We are looking for someone who is able to provide assistance to all levels of researchers and to do outreach that encourages best practice demographic research methods and successful grant proposals. These interpersonal skills are equally as important as the technical skills for this position.

In addition to research support functions, CSDE research staff are expected to develop their own area of expertise as well as peer networks within and outside of the UW. They participate significantly in research design and execution in collaboration with affiliate faculty, and serve substantial roles on sponsored projects. The Computational Demographer is expected to take a leadership role in identifying opportunities for, and development of, independent and collaborative research on topics consistent with the broad scientific goals of CSDE.

The Computational Demographer will be expected to serve as a technical mentor for the annual Data Science for Social Good (DSSG) summer program hosted by the eScience Institute. Graduate and undergraduate students from any university apply to DSSG and are then formed into teams which work on a data science project mentored by collaborators from the university, nonprofits, or government. The Computational Demographer will have a role in selecting a project that enhances either demographic methods or applied demography, and will mentor students working to complete the project objectives throughout the 10-week summer program.

Along with mentoring the DSSG students, the computational demographer would also augment existing activities for CSDE trainees, particularly building on activities initiated as part of the NIH T32 in Data Science and Demography Training (DSDT) program through presentations, short courses, and support for demography broadly at UW, such as the Computational Demography Working Group. The person’s expertise would also enhance activities in the development core by providing data handling expertise, reviewing grant applications, or helping write portions of the data science approach for external proposals.

The Computational Demographer, if interested, may also participate in the leadership of the Northwest Federal Statistics Research Data Center (NWFSRDC). The NWFSRDC is a secure enclave where approved researchers can access highly confidential data collected by federal agencies. If a candidate has experience working in an FSRDC environment and has interest in providing leadership for CSDE around these kinds of research infrastructure and related research projects, they should indicate that interest in their application letter for this position.

The first year of the position is supported 100%, jointly by CSDE and eScience. After the first year of this position, the Computational Demographer will be supported by eScience at 30% of their FTE, CSDE at 20-40% of their FTE (depending on their administrative roles), and expected to generate the remaining 30-50% of their FTE through participation on collaborative grants or other funding sources. This model has worked well with existing staff at both CSDE and eScience, as it encourages outreach, professional growth, and exposure to new topic areas. This position is ideal for a talented person with a PhD seeking a challenging and rewarding non-faculty academic research position, which offers ongoing opportunities for professional development including conference travel, training, and co-authorship in publications.


  • 25% - Identify opportunities for collaborative research and take a leadership role in all stages of the conceptualization of research topics, identification and development of appropriate data, design and execution of strategies to analyze these data, and dissemination of results to the scientific community with professional presentations and publications.

  • 20% - Support for the entire research cycle including study design and grant proposal writing; data collection, data management, data analysis, and data archiving; and manuscript writing and publication

  • 20% - Provide consulting and mentorship in statistical analysis, computational techniques and applications, research methodology, study design and implementation, and data management and archiving.

  • 10% - Provide workshops on statistical programming, data management, and data archiving to faculty and graduate students

  • 5% - Provide support for development of grant proposals

  • 5% - Provide outreach to the demography community

  • 5% - Develop new skills and areas of expertise as needed to support faculty research

  • 5% - Facilitate team communication and collaboration

  • 5% - Perform other duties as required, or if interested, including the possible leadership of the Northwest Federal Statistics Research Data Center (NWFSRDC).


  • Education requirement: Master’s in Statistics, Demography or equivalent coursework,

  • Experience requirement: Four or more years experience in a related area, or equivalent combination of education/experience

  • Programming Experience in Python and R

  • Cloud Computing Experience

  • Experience with design and use of relational databases

  • Experience with a variety of data linkage approaches

  • Teaching experience

  • Experience working on a team, desire to work collaboratively

  • Ability to communicate effectively with all levels of researchers

  • Experience working with faculty and graduate students

  • Demonstrated ability to work independently, take initiative, learn new skills, and do outreach

  • Experience with writing papers for publication

  • Experience writing grant proposals (in whole or part)


  • Doctoral-level education in social and behavioral science research methodology, or equivalent

  • Experience conducting social science research

  • Experience leveraging “Big Data” for scientific research (Data Science)

  • Experience with other statistical software (e.g., Mplus, ArcGIS, ATLAS.ti)

  • Experience with Natural Language Processing

  • Experience with qualitative research, particularly the analysis of qualitative data

  • Experience in visual displays of data

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