Data Scientist, Project Success and Innovation

American Institutes for Research•Chicago, NC

6d•Remote

About The Position

AIR is seeking a Data Scientist to join the Project Success and Innovation (PSI) team and the Employment and Economic Opportunity Program (EEOP). The role provides advanced technical expertise for innovation development, scaling efforts, and applied research and analytics across project teams. In PSI, the Data Scientist will help co-design, develop, and scale solutions that strengthen effective practices across technical and operational workflows. In EEOP, the Data Scientist will lead quantitative workflows, manage and quality-assure complex datasets, review junior staff code, and conduct advanced statistical analyses. These may include causal methods, such as experimental and quasi-experimental designs, and non-causal approaches, such as descriptive, predictive, and explanatory modeling. The role will also help expand AIR’s capacity to use machine learning responsibly and translate technical findings into actionable products for clients, partners, and other stakeholders. The position is divided approximately equally between PSI and EEOP responsibilities. Candidates may work remotely within the United States or from a U.S. office location; U.S. territories are excluded. Applicants must be authorized to work full time in the United States. Employment-based visa sponsorship is unavailable, and some project work may require candidates to meet residency requirements. AIR is a nonpartisan, not-for-profit organization founded in 1946 and headquartered in Arlington, Virginia. It conducts behavioral and social science research and provides technical assistance to address challenges in the United States and globally. AIR generates evidence and applies data-driven solutions to expand opportunities and improve lives.

Requirements

Master's or doctoral degree in a quantitative field, recently completed or nearing completion. Relevant fields include statistics, computational social science, education data science, economics, quantitative psychology, quantitative methods, data science, or a related discipline.
Coursework or applied experience in causal inference and statistical modeling.
Experience with complex survey data, sampling, and large-scale administrative, longitudinal, or linked datasets.
Experience applying quantitative methods to education, health, or social policy questions.
Experience or familiarity with machine learning and AI methods, including applications of large language models in research.
Interest in building tools for others to use and willingness to support their adoption.
Familiarity with data commonly used in applied social research, such as administrative records, longitudinal student or program data, survey data, or text.
Proficiency in R and/or Python.
Working knowledge of Stata.
Ability to communicate effectively with colleagues with varying levels of technical experience.
Ability to write clearly about technical work for non-technical readers.
Must be currently authorized to work in the United States on a full-time basis; employment-based visa sponsorship is not available.

Nice To Haves

Experience with restricted-use or confidential data.
Prior work building or maintaining code used by others.
Familiarity with version control (Git and Github) and an interest in reproducible research practices.
Familiarity with literate programming and reproducible reporting tools such as Quarto, R Markdown, or Jupyter.

Responsibilities

Build and maintain shared analytic tools, including code libraries, project templates, and pipelines that AIR research teams can adopt and reuse.
Write documentation, training materials, and onboarding guides to make modern tools accessible to staff with varying levels of technical experience.
Support project teams adopting new tools and workflows by answering questions, troubleshooting, and sharing recurring issues with the team.
Prototype and test tools and platforms, including machine learning and AI applications such as large language models, and help assess their fit and responsible use at AIR.
Maintain deployed tools by triaging issues, fixing bugs, and tracking user feedback.
Contribute to PSI work plans, timelines, and progress tracking, and communicate regularly with PSI leadership.
Conduct quantitative analyses for research and evaluation projects involving causal and non-causal research questions.
Lead analysis for discrete studies or analytic components with support from senior researchers.
Manage, clean, and quality-assure complex datasets from multiple sources.
Build and document reproducible analytic workflows and quality-control procedures.
Review analytic code produced by research associates and other junior staff, and support their development in reproducible analysis and documentation practices.
Contribute to study design and analytic planning discussions.
Draft analytic sections of client reports, technical memos, and presentations, and translate findings for non-technical audiences.
Track deliverables and timelines for assigned analytic tasks.
Contribute technical content to proposals.