Remote Data Scientist - Innovation & Project Success

American Institutes for Research; Chicago (IL); A distancia; USD 114.000 - 140.000; Jornada completa

Descripción de la vacante

American Institutes for Research seeks a Data Scientist to join the Project Success and Innovation (PSI) team and the Employment and Economic Opportunity Program (EEOP). The role supports innovation scaling, applied research, and analytics across AIR's workflows, with emphasis on reproducible environments and scalable tools.

The Data Scientist will lead end-to-end quantitative analyses, potentially using causal or non-causal methods, and help translate findings into actionable client-ready

Formación

• Master's or doctoral degree in a quantitative field, with coursework or applied experience in causal inference and statistical modeling.
• Experience with complex survey data, large-scale datasets, and health/education or social policy questions.
• Experience applying quantitative methods to research questions; ability to translate findings for non-technical audiences.

Responsabilidades

• Build and maintain shared analytic tools, pipelines, and documentation for AIR research teams.
• Lead quantitative analyses across PSI and EEOP projects, including causal and descriptive studies.
• Manage, clean, and quality-assure complex datasets from multiple sources.

Conocimientos

R and Python

Communication skills

Clear writing for non-technical

Statistical reasoning

Educación

Master's or Doctoral degree in a quantitative field

Coursework in causal inference

Herramientas

Git & GitHub

Quarto

R Markdown

Jupyter

Descripción del empleo

American Institutes for Research seeks a Data Scientist to join the Project Success and Innovation (PSI) team and the Employment and Economic Opportunity Program (EEOP). The role supports innovation scaling, applied research, and analytics across AIR's workflows, with emphasis on reproducible environments and scalable tools.

The Data Scientist will lead end-to-end quantitative analyses, potentially using causal or non-causal methods, and help translate findings into actionable client-ready