Data Scientist and Research Manager

Department

Crown Research Henly

About the Department

As one of the first schools of social work in the United States, the Crown Family School of Social Work, Policy, and Practice has shaped the field for more than 100 years. As part of the University of Chicago, the Crown Family School shares the University's core values and distinctive intellectual culture. Crown Family School faculty, staff, and students focus on identifying solutions to complex social problems through research, training, and direct community engagement. Guided by an interdisciplinary tradition, Crown Family School scholars conduct innovative research to address interconnected social challenges such as educational inequality, health disparities, crime and violence, poverty, and child and family welfare—working at multiple scales, from individual to policy-level interventions. More than 10,000 Crown Family School graduates in the US and globally hold leadership positions in academia, at nonprofits, in business, and at governmental agencies The Crown Family School is the home of several academic centers. These include the Susan and Richard Kiphart Center for Global Health and Social Development, the Kersten Institute for Urban Education, the Center for Climate, Health and Society, the Smart Decarceration Project, and the Employment Instability, Family Well-being, and Social Policy Network (EINet). More information may be found at https://crownschool.uchicago.edu/.

Job Summary

The position requires a highly motivated and experienced Data Scientist and Research Manager to support grant-funded research projects within Professor Julia Henly’s Policy Levers for Equitable Employment and Child Care Research Lab. The position will require data science/analytics management expertise, research staff supervision, as well as direct research responsibilities.

Data Science/Analytics Management:
Regarding data science/analytics management, the research data scientist will be responsible for planning and implementing reproducible research workflows across multiple projects, including:
Code management using version control (Git and GitHub);
Data management, such as designing permission-controlled data storage, resources, data linkage and harmonization, and other tasks to be determined;
Creating, implementing, and managing documentation strategies such as READMEs, GitHub repos, Wikis, codebooks, and internal memos;
Designing and building research data processing pipelines, incorporating tasks such as handling of missing data, qualitative coding and text processing, quality control testing, and imputation;
Building computational tools (such as user-defined functions, data-pulling scripts, and iterative analytic scripts) that persist over time and across projects to facilitate research;
Maintain confidentiality and comply with university data security policies across all projects.
Ensure data management and workflow procedures and in compliance with different policies, procedures, directives, and mandates.
The Research Data Scientist will also be responsible for identifying opportunities for improvement in the Henly Lab's analytical workflow and, through close listening and collaboration, ensuring solutions are tailored to current and future needs.

Research Staff Supervision:
Regarding staff supervision, the research data scientist will be responsible for training, supervising, and mentoring research assistants at undergraduate, master’s, and PhD levels who are part of the Policy Levers for Equitable Employment and Child Care lab. Including:
Support of learning and skill development of research staff.
Management of data-to-day research activities carried out by research team members.
Assisting the PI in the regular review of research staff performance.
The proper training of research staff in data security and compliance, and other research-related policies, procedures, directives, and mandates.


Research:
Regarding research, the research data scientist will be responsible for the following research activities:
Work with the research team to develop survey and interview protocols and identify and prepare administrative and archival data for analysis.
Assisting in the development of analytic strategies appropriate for diverse research designs and data structures, including quasi-experimental methods for causal inference.
Conduct advanced quantitative analysis, including modeling, propensity score matching, and weighting; analyze multilevel and longitudinal data.
Provide input to qualitative research activities led by other researchers on the team.
Share research findings with PI and other research team members on a regular basis to advance research efforts, written products, and plan follow-up work.
Contribute to reports, manuscripts, and presenta