Jobs / United States / OpenAI UK Ltd

OpenAI UK Ltd

Applied Scientist, Product Policy

San Francisco Posted today

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StatisticsSQLPythonRecruitment

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Sponsorship verdict

No sponsorship evidence yet

No government record and no wording either way. Not a refusal: ask the recruiter.

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  • Confirmed live todayWhen a source last listed this job as open.
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No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

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About the role

About the Team The Product Policy research team studies how people use and experience AI, generating evidence to inform product, policy, and safety decisions. We partner with researchers, data scientists, engineers, and cross-functional teams to understand AI’s effects on people’s lives, including well-being, youth experiences, and social and emotional use. About the Role We’re looking for an Applied Scientist to help design, execute, and analyze large-scale longitudinal research on AI use and well-being. You’ll work closely with research leads to translate scientific questions into rigorous studies, combining survey responses with behavioral data to understand how people’s experiences with AI evolve over time. This is a hands-on role spanning applied analysis, research methods, and study execution. You’ll build reproducible analytical workflows, assess data quality and representativeness, and help establish reliable infrastructure for ongoing panel research. Your work will support research involving teens and adults, including studies of AI companionship, and help turn findings into useful evidence for product, policy, and safety partners. In this role, you will: - Partner with research leads to develop study designs, survey measures, and analysis plans for longitudinal research on AI use and well-being. - Conduct statistical analyses of survey and behavioral data, including evaluating selection and response bias, account-linkage patterns, and sample representativeness. - Prepare and validate analysis-ready datasets, implement analysis plans, and produce clear summaries and visualizations using reproducible workflows. - Develop quality checks across survey programming, fieldwork, data delivery, and repeated study waves to identify inconsistencies and improve research reliability. - Own day-to-day study execution, coordinating timelines, vendors, and cross-functional dependencies to deliver high-quality research on schedule. - Collaborate with data engineering and privacy partners on responsible account linkage, data flows, logging, and retention. - Support human-subjects research review, including Institutional Review Board (IRB) documentation, protocol amendments, and consent and assent processes. - Maintain clear documentation of methods, measures, assumptions, and protocol changes, and contribute analysis and methodological materials for research readouts, publications, and peer review. - Communicate findings, limitations, and practical recommendations to technical and nontechnical partners, and build reusable tools and processes that improve future studies. You might thrive in this role if you: - Have experience conducting applied quantitative research in social science, behavioral science, human-computer interaction, statistics, or a related field. - Bring strong foundations in survey research and statistical analysis, with experience working on panel studies, longitudinal datasets, or human-subjects research. - Are comfortable using SQL and Python or R to prepare data, implement analyses, validate results, and create visualizations. - Understand how recruitment, missing data, participant attrition, and measurement choices can affect the interpretation and generalizability of findings. - Can move between analytical work and practical execution, managing multiple timelines and collaborators while maintaining scientific rigor. - Communicate clearly, lead with the key takeaway, and connect methodological choices and findings to the decisions they inform. - Exercise sound judgment when working with sensitive data and research involving minors. - Enjoy building structure in ambiguous environments and collaborating across disciplines. Nice to Have - Experience combining survey responses w

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