Job description

AIR is seeking a Data Scientist to join the Project Success and Innovation (PSI) team and the Employment and Economic Opportunity Program (EEOP ), providing advanced technical expertise to support innovation development, scaling efforts, and applied research and analytics across project teams. Within PSI, the Data Scientist will contribute to AIR’s Innovation Lab by co-designing, developing, and scaling solutions that strengthen and amplify effective practices across diverse technical and operational workflows. Within EEOP, the Data Scientist will lead end-to-end quantitative workflows, including establishing and maintaining reproducible analytic environments, managing and quality assuring complex datasets, reviewing and strengthening junior staff members’ code, and conducting advanced statistical analyses. These analyses may include causal approaches, such as experimental and quasi-experimental designs, as well as non-causal approaches, including descriptive, predictive, and explanatory modeling. The Data Scientist will also help expand AIR’s capacity to responsibly apply machine learning methods in support of both research and operational objectives, while translating technical findings into clear, actionable products for clients, partners, and other stakeholders. Candidates hired for the position may work remotely within the United States (U.S.) or from one of our U.S. office locations. This does not include U.S. territories. About AIR: Founded in 1946 and headquartered in Arlington, Virginia, the American Institutes for Research (AIR) is a nonpartisan, not-for-profit organization that conducts behavioral and social science research and delivers technical assistance to address some of the most pressing challenges in the United States and globally. We generate evidence and apply data-driven solutions that expand opportunities and improve lives for all. Responsibilities The responsibilities for the position include: PSI (approximately 50 %) Build and maintain shared analytic tools, including code libraries, project templates, and pipelines, that research teams across AIR can adopt and reuse. Write documentation, training materials, and onboarding guides that make modern tooling accessible to staff with varying levels of technical experience. Support project teams adopting new tools and workflows by answering questions, troubleshooting, and relaying recurring problems back to the team. Prototype and test new tools and platforms, including machine learning and AI applications such as large language models, and help assess their fit and responsible use at AIR. Help maintain deployed tools by triaging issues, fixing bugs, and tracking user feedback. Contribute to work plans, timelines, and progress tracking for PSI activities, communicating regularly with PSI leadership. EEOP (approximately 50 %) Conduct quantitative analyses on research and evaluation projects, including analyses for 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. Qualifications: Education, Knowledge, and Experience Master's or doctoral degree in a quantitative field, including statistics, computational social science, education data science, statistics, economics, quantitative psychology, quantitative methods, data science, or a related discipline, 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. 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. Skills Proficiency in R and/or Python. Working knowledge of Stata. 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. Ability to communicate effectively with colleagues who have varying levels of technical experience. Ability to write clearly about technical work for non-technical readers. Disclosures: Applicants must be currently authorized to work in the U.S. on a full-time basis. Employment-based visa sponsorship (including H-1B sponsorship) is not available for this position. Depending on project work, qualified candidates may need to meet certain residency requirements. American Institutes for Research is an equal employment opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without discrimination on the basis of any characteristic protected by applicable federal, state or local law, including, but not limited to, actual or perceived race (including traits historically associated with race, such as hair texture, hair type, and protective hairstyles such as braid, locs, and twists), creed, color, religion, alienage or national origin, ancestry, citizenship status, age, physical or mental disability, medical condition (e.g., cancer), sex, gender, gender identity or expression (including transgender status), sexual orientation, marital status, civil union status, pregnancy, childbirth or related medical conditions, genetic information, or military or veteran status. AIR adheres to strict child safeguarding principles. All selected candidates will be expected to adhere to these standards and principles and will therefore undergo reference and background checks. AIR maintains a drug-free work environment. ACCESSIBILITY NOTICE: If you need a reasonable accommodation for any part of the employment process due to a physical or mental disability, please send an email to Taliba Boone at tboone@air.org or call 202.403.5000. Fraudulent Job Scams Warning & Disclaimer: AIR is aware of individuals falsely presenting themselves as AIR representatives. Fraudulent job scams seek to extract sensitive information or money from victims. To protect yourself, please be aware that AIR recruitment will only email you from an “@air.org” domain. Please take extra caution while examining the email address, for example jdoe@air.org is correct and jdoe@aircareers.org is not a legitimate AIR email address. If you are unsure of the legitimacy of a communication you have received, please reach out to recruitment@air.org. If you see a job scam, or lose money to one, report it to the Federal Trade Commission (FTC) at ReportFraud.ftc.gov. You can also report it to your state attorney general. Find out more about how to avoid scams atftc.gov/scams. #LI-MP1 #LI-REMOTE AIR’s Total Rewards Program, is designed to reward our staff competitively and motivate them to achieve our critical mission. This position offers the anticipated annual salary as listed. Salary offers are made based on internal equity within the institution and external equity with competitive markets. Please note this is the annual salary range for candidates that are based in the United States. Anticipated Annual Salary Range $114,000 — $140,000 USD

Requirements

Must-haves

Candidates hired for the position may work remotely within the United States (U.S.) or from one of our U.S. office locations. This does not include U.S. territories.
PSI (approximately 50 %)
Build and maintain shared analytic tools, including code libraries, project templates, and pipelines, that research teams across AIR can adopt and reuse.
Write documentation, training materials, and onboarding guides that make modern tooling accessible to staff with varying levels of technical experience.
Support project teams adopting new tools and workflows by answering questions, troubleshooting, and relaying recurring problems back to the team.
Prototype and test new tools and platforms, including machine learning and AI applications such as large language models, and help assess their fit and responsible use at AIR.
Help maintain deployed tools by triaging issues, fixing bugs, and tracking user feedback.
Contribute to work plans, timelines, and progress tracking for PSI activities, communicating regularly with PSI leadership.
EEOP (approximately 50 %)
Conduct quantitative analyses on research and evaluation projects, including analyses for 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.
Education, Knowledge, and Experience
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 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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonaccessibility

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