Remote Data Scientist: Causal Analytics & Innovation

OpenKrill; United States; Remote; USD 95,000 - 130,000; Full time

Job summary

AIR is seeking a Data Scientist to support the Project Success and Innovation and EEOP programs, performing end-to-end quantitative work, reproducible analytics, and advanced statistical analyses. The role involves building tools, mentoring staff, and translating findings for clients and stakeholders.

The position may be remote within the United States or based in a U.S. office, with a focus on responsible ML and data-driven decision making across AIR projects.

Qualifications

• Master’s or doctoral degree in a quantitative field with coursework in causal inference and statistical modeling.
• Experience with complex survey data, large-scale datasets, and data management.
• Experience applying quantitative methods to education, health, or social policy.
• Experience or familiarity with machine learning and AI, including LLMs in research.
• Interest in building reusable tools and supporting end users.

Responsibilities

• Develop and maintain reproducible analytic workflows and tools across AIR teams.
• Lead quantitative analyses, including causal and non-causal research questions.
• Review junior staff code and strengthen reproducible analysis practices.
• Draft analytic sections of client reports and translate findings for non-technical audiences.
• Contribute to study design, proposals, and timelines.

Skills

R/Python

Stata

Git/GitHub

Literate programming

Quarto/R Markdown/Jupyter

Communication of technical work to non

Education

Master’s or PhD in a quantitative field

Tools

Git/GitHub

Quarto

R Markdown

Jupyter

Python/R

Job description

AIR is seeking a Data Scientist to support the Project Success and Innovation and EEOP programs, performing end-to-end quantitative work, reproducible analytics, and advanced statistical analyses. The role involves building tools, mentoring staff, and translating findings for clients and stakeholders.

The position may be remote within the United States or based in a U.S. office, with a focus on responsible ML and data-driven decision making across AIR projects.