Data Scientist, Learning Supports
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Job Description
Data Scientist providing foundational and advanced analytic support for Learning Supports program. Leading quantitative workflows and translating results into actionable insights for clients.
Responsibilities:
- Lead or contribute to the design and execution of rigorous quantitative research and evaluation projects across multiple Learning Supports 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.
Requirements:
- A Master’s degree with 4 years of relevant quantitative research experience or a Bachelor’s Degree with 5 years of relevant experience.
- 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 preferred, but not required.
- Experience working in education or K–12 research contexts is preferred, but not required.
Benefits:
- AIR’s Total Rewards Program is designed to reward our staff competitively and motivate them to achieve our critical mission.
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