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OpenAI's Product Policy research team is seeking an Applied Scientist to design, execute, and analyze large-scale longitudinal research on AI use and well-being. This hands-on role combines applied statistical analysis, research methodology, and study execution to generate evidence informing product, policy, and safety decisions.
You will partner with research leads to develop rigorous study designs and analysis plans for longitudinal research examining how people's experiences with AI evolve over time. Your responsibilities include conducting statistical analyses of survey and behavioral data, assessing selection and response bias, evaluating sample representativeness, and preparing analysis-ready datasets using reproducible workflows. You'll implement quality checks across survey programming, fieldwork, and data delivery to identify inconsistencies and improve research reliability.
Day-to-day execution is a core part of this role: you'll own study timelines, coordinate with vendors and cross-functional teams, and ensure high-quality research delivery on schedule. You'll collaborate with data engineering and privacy partners on responsible account linkage, data flows, and retention protocols. Additionally, you'll support human-subjects research review processes, including IRB documentation, protocol amendments, and consent procedures for studies involving teens and adults, including research on AI companionship.
Communication and documentation are essential. You'll maintain clear records of methods, measures, and protocol changes, contribute to research publications and peer review, and translate findings into actionable recommendations for technical and non-technical stakeholders. You'll also build reusable tools and processes that improve future research.
QUALIFICATIONS:
Required:
- Experience conducting applied quantitative research in social science, behavioral science, human-computer interaction, statistics, or related field
- Strong foundations in survey research and statistical analysis, with experience on panel studies, longitudinal datasets, or human-subjects research
- Proficiency in SQL and Python or R for data preparation, analysis implementation, validation, and visualization
- Understanding of how recruitment, missing data, participant attrition, and measurement choices affect interpretation and generalizability
- Ability to balance analytical work with practical execution, managing multiple timelines and collaborators while maintaining scientific rigor
- Clear communication skills; ability to lead with key takeaways and connect methodological choices to decisions they inform
- Sound judgment when working with sensitive data and research involving minors
- Comfort building structure in ambiguous environments and collaborating across disciplines
Nice to Have:
- Experience combining survey responses with behavioral or product data
- Familiarity with preregistration, measure validation, reproducible research, and academic publication or peer-review processes
- Experience working with research vendors, survey platforms, or IRB and ethics-review partners
- Research experience in AI-human interaction, digital well-being, youth development, or social and emotional uses of technology
