Data Scientist, Project Success and Innovation

American Institutes for Research•Sacramento, CA

7d•$114,000 - $140,000•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. Within PSI, the Data Scientist will help co-design, develop, and scale solutions that strengthen effective practices across technical and operational workflows. Within EEOP, the Data Scientist will lead quantitative workflows, establish reproducible analytic environments, manage and quality-assure complex datasets, review junior staff members’ code, and conduct advanced statistical analyses. The work may involve causal methods, including experimental and quasi-experimental designs, and non-causal methods, including descriptive, predictive, and explanatory modeling. The Data Scientist will also help expand AIR’s responsible use of machine learning in research and operations and translate technical findings into actionable products for clients, partners, and other stakeholders. The role is divided approximately equally between PSI and EEOP. 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 not available, and project work may require certain residency requirements.

Requirements

Master’s or doctoral degree in a quantitative field, recently completed or nearing completion.
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.
Proficiency in R and/or Python.
Working knowledge of Stata.
Ability to communicate effectively with colleagues who have varying levels of technical experience.
Ability to write clearly about technical work for non-technical readers.
Applicants must be currently authorized to work in the United States on a full-time basis.

Nice To Haves

Experience or familiarity with machine learning and AI methods, including applications of large language models in research.
Interest in building tools that other people use, and willingness to support them in doing so.
Familiarity with data common in applied social research, such as administrative records, longitudinal student or program data, survey data, or text.
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, for reuse by AIR research teams.
Write documentation, training materials, and onboarding guides to make modern tooling accessible to staff with varying technical experience.
Support project teams adopting new tools and workflows by answering questions, troubleshooting, and relaying recurring issues to the team.
Prototype and test tools and platforms, including machine learning and AI applications such as large language models, and help assess their suitability and responsible use at AIR.
Help 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, including causal and non-causal research questions.
Serve as lead analyst on 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 help translate findings for non-technical audiences.
Track deliverables and timelines for assigned analytic tasks.
Contribute technical content to proposals.