Data Scientist, Education Systems and Policy

• Lead or contribute to the design and execution of rigorous quantitative research and evaluation projects across multiple Education Systems and Policy initiatives.
• Apply appropriate quantitative methods to collect, manage, analyze, and interpret data, including leading inferential analyses for both causal and non‑causal research questions.
• Develop, maintain, and document reproducible and collaborative analytic workflows, including data management processes and quality control procedures.
• Review, quality‑check, and strengthen analytic code and outputs produced by junior staff; provide guidance and mentorship to promote best practices in analysis and documentation.
• Translate complex analytic findings into clear, actionable insights for client reports, technical memos, presentations, and briefings.
• Manage discrete project tasks or analytic components, including planning timelines, tracking deliverables, and coordinating with project leadership and clients.
• Support proposal development by contributing to technical and analytic sections and helping shape study design and analytic approaches.
• Engage with internal and external stakeholders through meetings and dissemination activities, contributing to a collaborative and inclusive team environment.

Qualifications:

Education, Knowledge, and Experience

• Master's or doctoral degree in statistics, mathematics, computer science, computational social science, education data science, or a related discipline.
• Demonstrated understanding of education policy, education systems and school and district finance.
• Demonstrated experience supporting or leading quantitative research or evaluation projects, preferably for public-sector, nonprofit, or education-focused clients.
• Experience designing and executing inferential analyses and contributing to research reports, briefs, or presentations.
• Experience managing and maintaining analytic datasets, including documentation, quality control, and reproducible workflows.
• Experience with machine learning methods or interest in applying ML to support causal and non-causal research.
• Experience working in education or K–12 research contexts.

Skills

• Proficiency in R, Stata, or Python for applied quantitative analysis.
• Ability to review, quality-check, and improve others’ analytic code and support junior staff through guidance and mentorship.
• Strong analytical and problem-solving skills, with attention to accuracy and research, best practices.
• Strong communication skills, with the ability to translate complex analyses for technical and non-technical audiences.
• Ability to manage multiple tasks and deadlines independently while contributing to a collaborative team environment.