Data Scientist, Project Success and Innovation

American Institutes for Research

. Build and maintain shared analytic tools, including code libraries, project templates, and pipelines .

Posted 10/16/2026full-timeUnited StatesMid-LevelSenior💰 $114,000 - $140,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in quantitative analysis, machine learning, and AI methods, with proficiency in R and Python for data management and analysis. Capable of effectively communicating complex findings to diverse audiences and supporting project teams in adopting new analytic tools and workflows.

Highest-signal resume keywords

Proficiency In RProficiency In PythonExperience With Machine LearningExperience With Causal InferenceExperience With Complex Survey Data

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Quantitative AnalysisStatistical ModelingData ManagementData CleaningQuality AssuranceAnalytic Workflow DocumentationCode ReviewTechnical WritingAnalytic PlanningPrototype Testing

Soft Skills

Effective CommunicationCollaborationProblem-SolvingMentoring

Tools & Technologies

StataGitGithubQuartoR MarkdownJupyter

Industry Keywords

Applied Social ResearchEducation PolicyHealth PolicySocial PolicyLongitudinal DataConfidential DataChild Safeguarding Standards

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build and maintain shared analytic tools, including code libraries, project templates, and pipelines
Write documentation, training materials, and onboarding guides
Support project teams adopting new tools and workflows by answering questions and troubleshooting
Prototype and test machine learning and AI tools, including large language models, and assess responsible use
Maintain deployed tools by triaging issues, fixing bugs, and tracking user feedback
Contribute to PSI work plans, timelines, and progress tracking; communicate with PSI leadership
Conduct quantitative analyses for causal and non-causal research questions
Serve as lead analyst on discrete studies or analytic components
Manage, clean, and quality-assure complex datasets
Build and document reproducible analytic workflows and quality control procedures
Review junior staff analytic code and support their development
Contribute to study design and analytic planning
Draft analytic sections of client reports, technical memos, and presentations
Translate findings for non-technical audiences
Track deliverables and timelines for assigned analytic tasks
Contribute technical content to proposals

Requirements

What you’ll need

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
Experience or familiarity with machine learning and AI methods, including large language models in research
Familiarity with data common in applied social research
Experience with restricted-use or confidential data
Prior work building or maintaining code used by others
Proficiency in R and/or Python
Working knowledge of Stata
Familiarity with Git and Github and reproducible research practices
Familiarity with Quarto, R Markdown, or Jupyter
Ability to communicate effectively with colleagues with varying technical experience
Ability to write clearly about technical work for non-technical readers
Currently authorized to work in the U.S. on a full-time basis
Employment-based visa sponsorship is not available
May need to meet certain residency requirements depending on project work
Selected candidates will undergo reference and background checks
Must adhere to AIR’s child safeguarding standards and principles

Benefits

Comp & perks

AIR’s Total Rewards Program
Equal employment opportunity/affirmative action employer
Reasonable accommodation for the employment process
Drug-free work environment
Child safeguarding standards and reference/background checks