Quantitative UX Researcher, Product Policy

📍 Job Overview

Job Title: Quantitative UX Researcher, Product Policy

Company: OpenAI

Location: San Francisco, California, United States

Job Type: Full-Time

Category: Product Policy & Operations Research

Date Posted: November 06, 2025

Experience Level: 7+ Years (with AI-driven inference of 10+ years for strategic implications)

Remote Status: Hybrid

🚀 Role Summary

• Drive a data-driven policy development culture by applying quantitative user research methodologies to inform product policy lifecycle management.

• Define, track, and operationalize key performance indicators (KPIs) and "north star" metrics to measure the efficacy and success of OpenAI's product policies and enforcement strategies.

• Collaborate cross-functionally with Safety Systems, Intelligence & Investigations, and Global Affairs teams to integrate research insights and coordinate policy initiatives.

• Translate complex user behavior and expectations into actionable insights that guide short- and long-term policy priorities and strategic decision-making.

📝 Enhancement Note: The "Product Policy" aspect of this role, combined with the "Quantitative UX Researcher" title and the "Measurement team" context, strongly indicates a focus on operationalizing policy. This involves not just research but also the implementation and ongoing monitoring of policy effectiveness through data. The AI-inferred experience level of 10+ years likely reflects the seniority and strategic impact expected from an early member of this team, capable of establishing foundational processes and metrics.

📈 Primary Responsibilities

• Design and execute quantitative research studies to understand user expectations, behaviors, and pain points related to OpenAI's products and services, directly informing policy frameworks.

• Develop and implement robust measurement frameworks and dashboards to track the performance of product policies, identifying areas for optimization and risk mitigation.

• Analyze large datasets of user interactions, feedback, and incident reports to uncover trends, diagnose policy-related issues, and recommend data-backed solutions.

• Partner closely with Product Managers, Engineers, and Policy Leads to translate research findings into concrete policy recommendations, product adjustments, and operational improvements.

• Communicate research insights, policy performance analyses, and strategic recommendations effectively to diverse stakeholders, including executive leadership, through clear reports, presentations, and data visualizations.

• Contribute to the definition of "north star" metrics that encapsulate user value and policy adherence, ensuring alignment with OpenAI's mission and responsible AI principles.

• Proactively identify and investigate emerging issues related to AI policy and user experience, proposing innovative research approaches to address them.

📝 Enhancement Note: The responsibilities are framed to emphasize the operationalization of policy through quantitative research. This includes not just research design but also the crucial steps of defining success metrics, implementing tracking mechanisms, analyzing policy performance, and driving action based on data. The mention of "Safety Systems" and "Intelligence & Investigations" highlights the critical role of this research in ensuring responsible AI deployment.

🎓 Skills & Qualifications

Education:

• A Bachelor's or advanced degree (Master's or Ph.D.) in a quantitative field such as Computer Science, Statistics, Economics, Psychology, Cognitive Science, Human-Computer Interaction, or a related discipline.

Experience:

• 7+ years of progressive experience in quantitative user experience research, product analytics, data science, or a similar role focused on understanding user behavior and informing product strategy.

• Demonstrated experience navigating ambiguous environments and establishing research practices from the ground up, especially in early-stage teams or product areas.

Required Skills:

• Quantitative Research Design: Expertise in designing, conducting, and analyzing large-scale quantitative studies (e.g., surveys, A/B tests, log analysis, experimental designs).

• Statistical Analysis & Modeling: Proficiency in statistical software and techniques (e.g., R, Python with libraries like SciPy, NumPy, Pandas, Statsmodels) for data analysis, modeling, and hypothesis testing.

• User Behavior Analysis: Deep understanding of how to analyze user interaction data, identify behavioral patterns, and infer user needs or policy compliance issues.

• Policy Metrics Definition: Ability to define, operationalize, and track key metrics that effectively measure the impact and success of product policies.

• Cross-Functional Collaboration: Excellent interpersonal and communication skills to effectively partner with product managers, engineers, policy experts, and leadership across different departments.

• Data Storytelling: Skill in communicating complex quantitative findings and their implications clearly and persuasively to both technical and non-technical audiences.

Preferred Skills:

• Background in social science disciplines (e.g., economics, psychology, sociology) with a strong quantitative focus.

• Familiarity with major AI policy challenges, ethical considerations, and the regulatory landscape impacting AI development and deployment.

• Experience with large language models (LLMs), AI product development, or the AI research landscape.

• Expertise in causal inference techniques and their application in product and policy research.

• Experience with survey platforms (e.g., Qualtrics, SurveyMonkey) and user feedback tools.

📝 Enhancement Note: The emphasis on "strategic insights that extend beyond the paradigm of statistical significance testing" suggests a need for candidates who can interpret data in a broader business and policy context, moving beyond purely academic statistical rigor to practical, impactful decision-making. The mention of "founding user experience researcher" points to a need for proactive, self-starting individuals capable of shaping the function.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing at least 3-5 impactful quantitative research projects, with a strong emphasis on how data insights led to tangible policy improvements or strategic shifts.

• Each project should clearly articulate the problem statement, research methodology employed, key findings, and the measurable impact or outcome achieved.

• Highlight experience in defining and tracking operational or policy-related metrics, illustrating how these metrics were used to monitor performance and drive continuous improvement.

• Include examples of how you've translated complex user data into actionable recommendations for product or policy teams, showcasing your ability to influence decision-making.

Process Documentation:

• Evidence of developing or refining research processes to ensure scalability, rigor, and efficiency in data collection and analysis for policy evaluation.

• Examples of creating documentation for research methodologies, data dictionaries, or metric definitions to ensure consistency and knowledge sharing within a team.

• Demonstrate an understanding of how to integrate quantitative insights into existing product development and policy enforcement workflows.

📝 Enhancement Note: For a role focused on operationalizing policy through data, a portfolio must go beyond just research design. It needs to show evidence of impact, the ability to define and track operational metrics, and how research directly influenced policy or product decisions. The emphasis on "early member of the Measurement team" suggests the need for candidates who can help build and document processes.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Comprehensive health, dental, and vision insurance plans.

• 401(k) retirement savings plan with company match.

• Generous paid time off (PTO) and company holidays.

• Relocation assistance to San Francisco for eligible candidates.

• Stock options or equity grants (typical for senior roles at tech companies like OpenAI).

• Professional development opportunities, including conference attendance and training.

Working Hours:

• This is a full-time role, typically requiring 40 hours per week. While the role is hybrid with 3 days in the office, flexibility may be offered based on project needs and team agreements, accommodating the demands of data analysis and cross-functional collaboration.

📝 Enhancement Note: The salary range is explicitly provided. The benefits are inferred based on standard offerings for senior technical roles at leading technology companies in high-cost-of-living areas like San Francisco. The "early member of the Measurement team" context suggests potential for significant impact and, therefore, potentially competitive equity.

🎯 Team & Company Context

🏢 Company Culture

Industry: Artificial Intelligence Research & Deployment

Company Size: OpenAI is a rapidly growing organization, with a significant number of employees (likely in the 1,000-5,000+ range based on its impact and public profile), indicating a dynamic and fast-paced environment. This size allows for specialized teams like Product Policy while fostering a culture of innovation.

Founded: OpenAI was founded in 2015, giving it a mature yet agile presence in the AI landscape, focused on both cutting-edge research and practical product deployment.

Team Structure:

• The Product Policy team is described as being responsible for the development, implementation, enforcement, and communication of policies governing OpenAI's services. This team likely comprises policy experts, legal counsel, researchers, and operational specialists.

• The role sits within the "Measurement team" within Product Policy, suggesting a specialized group focused on data-driven evaluation and reporting for policy success.

Methodology:

• A strong emphasis on a "data-driven policy development culture," indicating that decisions are grounded in empirical evidence and user insights.

• Focus on defining and tracking "policy-success metrics" and "north star" metrics to ensure accountability and measure impact.

• Commitment to responsible AI development and deployment, with policy playing a crucial role in mitigating risks and ensuring benefits for all humanity.

Company Website: https://openai.com/

📝 Enhancement Note: The description of the Product Policy team's mandate and the "Measurement team" context paints a picture of a sophisticated, data-centric operational structure designed to manage the complexities of AI product deployment. The cross-functional collaboration points to a matrixed operational environment where influence and coordination are key.

📈 Career & Growth Analysis

Operations Career Level: This is a senior-level individual contributor role, likely categorized as a Principal or Lead Quantitative UX Researcher, given the 7+ years of experience requirement and the expectation to be an "early member" of a critical team. The role demands strategic thinking and the ability to shape foundational processes and metrics in a nascent area.

Reporting Structure: The role reports into the Product Policy team, specifically within the newly forming Measurement team. This implies a direct reporting line to a manager or lead overseeing policy measurement and analytics, with significant interaction with senior leaders in Product Policy, Safety, and Engineering.

Operations Impact: This role has a direct and significant impact on the responsible deployment of OpenAI's AI technologies. By providing data-driven insights into user behavior and policy effectiveness, the Quantitative UX Researcher will directly influence product strategy, policy enforcement, risk mitigation, and the overall user experience, contributing to OpenAI's mission of benefiting humanity.

Growth Opportunities:

• Leadership in Measurement: As an early member of the Measurement team, there's a strong opportunity to define its strategy, processes, and impact, potentially leading to future team leadership roles.

• Specialization in AI Policy: Deepen expertise in the unique quantitative research challenges and policy considerations specific to AI and large language models, becoming a go-to expert in this emerging field.

• Cross-Functional Influence: Expand influence across Product, Engineering, Safety, and Legal teams by consistently delivering high-impact research that shapes critical policy decisions.

• Skill Development: Opportunities to learn and apply advanced quantitative methods, causal inference, and data science techniques in a cutting-edge AI environment.

📝 Enhancement Note: The "early member" status is a key indicator of growth potential. This role is positioned to build and define processes, offering a unique opportunity for an operations-minded researcher to establish a framework for policy measurement and impact assessment within a leading AI company.

🌐 Work Environment

Office Type: OpenAI operates on a hybrid work model, requiring 3 days per week in the San Francisco office. This suggests a collaborative in-office environment balanced with the flexibility of remote work for focused tasks.

Office Location(s): The role is based in San Francisco, California, a major hub for technology and innovation, offering access to a vibrant professional ecosystem.

Workspace Context:

• The San Francisco office likely provides a modern, collaborative workspace designed for team interaction, brainstorming, and focused individual work.

• Access to advanced computing resources, internal data platforms, and standard productivity tools will be essential for quantitative research and data analysis.

• Opportunities for close collaboration with cross-functional teams (Policy, Safety, Engineering, Product) will be abundant, fostering a dynamic and interconnected work environment.

Work Schedule: The standard 40-hour work week is expected, with the hybrid model allowing for structured in-office collaboration days and remote work days. The nature of policy development and incident response may occasionally require flexibility outside standard hours.

📝 Enhancement Note: The hybrid model with specified in-office days is a critical detail for operations professionals who value structured collaboration and dedicated time for focused analysis. This setup aims to combine the benefits of in-person synergy with the efficiency of remote work.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and portfolio for relevant quantitative research experience, policy-related insights, and communication skills.

• Hiring Manager Interview: A discussion focused on your experience, approach to quantitative UX research in ambiguous policy contexts, and alignment with OpenAI's mission. Expect questions about your ability to define metrics and influence strategy.

• Technical/Research Deep Dive: A session where you'll likely present a past project from your portfolio, detailing your methodology, findings, and impact. This may involve a take-home assignment or a live case study focused on a policy-related problem.

• Cross-Functional Interviews: Meetings with potential collaborators from Product, Engineering, Safety, or other Policy teams to assess your ability to work cross-functionally and communicate effectively.

• Executive Interview: A final conversation with senior leadership to assess strategic thinking, cultural fit, and overall alignment with OpenAI's long-term vision.

Portfolio Review Tips:

• Quantify Impact: For each project, clearly state the problem, your approach, the key insights, and most importantly, the measurable impact or outcome. Use numbers and data to demonstrate the value of your work.

• Highlight Policy Relevance: Select projects that demonstrate your ability to tackle complex, ambiguous problems, especially those involving user behavior, system design, or policy implications. If you have direct policy experience, emphasize it.

• Showcase Metric Definition: Include examples where you defined, implemented, or tracked key metrics. Explain why these metrics were chosen and how they guided decisions.

• Structure for Clarity: Organize your portfolio logically. For each project, use a clear structure: Problem -> Research Questions -> Methodology -> Findings -> Recommendations -> Impact.

• Prepare for Deep Dives: Be ready to discuss your choices in methodology, the trade-offs you made, and how you handled challenges or unexpected results in your presented projects.

Challenge Preparation:

• Policy Scenario Analysis: Be prepared for a case study or hypothetical scenario involving a policy challenge. Focus on how you would approach defining the problem, identifying necessary data, designing research to gather insights, and measuring the success of potential solutions.

• Metric Design: Practice designing "north star" metrics or KPIs for a given product policy or feature. Consider what behaviors or outcomes you'd want to influence and how to measure them accurately.

• Communication of Complex Data: Prepare to explain a complex quantitative finding to a non-technical audience, focusing on the "so what?" and actionable implications for policy.

📝 Enhancement Note: The emphasis on "policy development culture," "policy-success metrics," and "user expectations and behavior" means that interview preparation should heavily lean into demonstrating how quantitative research directly informs and measures the effectiveness of policy. The portfolio review will be critical for showcasing this applied capability.

🛠 Tools & Technology Stack

Primary Tools:

• Statistical Software: R, Python (with libraries like Pandas, NumPy, SciPy, Statsmodels, Scikit-learn).

• Data Warehousing/Querying: SQL for data extraction and manipulation from large databases.

• Survey Platforms: Qualtrics, SurveyMonkey, or similar for designing and deploying quantitative surveys.

• Experimentation Platforms: Tools for A/B testing and experimental design (internal or common platforms).

Analytics & Reporting:

• Data Visualization Tools: Tableau, Looker, Power BI, or Python libraries (Matplotlib, Seaborn, Plotly) for creating insightful dashboards and reports.

• Product Analytics Tools: Experience with tools that track user behavior and product engagement (e.g., Amplitude, Mixpanel, Google Analytics - though OpenAI likely uses internal equivalents).

CRM & Automation:

• While not a direct CRM role, understanding how user data is managed and utilized within systems like CRMs or internal data platforms is beneficial for context.

• Familiarity with workflow automation concepts can be helpful for understanding how research insights are operationalized.

📝 Enhancement Note: This role is inherently data-intensive. Proficiency in statistical programming languages and SQL is non-negotiable for data extraction and analysis. Experience with data visualization tools is crucial for communicating findings effectively. The company's scale suggests sophisticated internal data infrastructure, so adaptability to proprietary tools is also implied.

👥 Team Culture & Values

Operations Values:

• Data-Driven Decision Making: A core value is the reliance on empirical evidence and quantitative insights to guide policy development and product strategy.

• User-Centricity: A commitment to understanding and prioritizing user needs and expectations in the design and enforcement of policies.

• Responsible Innovation: A strong ethical compass focused on ensuring that AI benefits humanity and is deployed safely and responsibly.

• Collaboration & Transparency: Fostering an environment where teams work together openly, share insights, and constructively challenge each other to achieve the best outcomes.

• Impact & Ownership: A drive to make a measurable impact, taking ownership of challenges and delivering solutions that advance OpenAI's mission.

Collaboration Style:

• Cross-Functional Integration: The role requires seamless collaboration with diverse teams, necessitating strong communication, empathy, and the ability to bridge technical, policy, and strategic perspectives.

• Partnership with Product & Engineering: Working closely with product and engineering teams to understand technical constraints and opportunities, and to integrate research findings into product roadmaps and policy implementations.

• Insight Sharing: A culture of proactively sharing research findings and analytical insights across the organization to inform broader strategic discussions.

📝 Enhancement Note: The values emphasize a blend of scientific rigor, ethical responsibility, and collaborative execution, which are paramount for a role that bridges research, policy, and product operations in the AI space.

⚡ Challenges & Growth Opportunities

Challenges:

• Ambiguity of AI Policy: Navigating the rapidly evolving and often undefined landscape of AI policy, requiring proactive research and the establishment of new frameworks.

• Measuring Subjective Concepts: Quantifying user perceptions, trust, and ethical concerns related to AI, which can be abstract and difficult to measure directly.

• Data Scale & Complexity: Working with massive, complex datasets and ensuring data quality and integrity for reliable policy analysis.

• Balancing Innovation and Safety: Finding the optimal balance between enabling cutting-edge AI innovation and ensuring robust safety, fairness, and responsible use through policy.

• Cross-Functional Alignment: Gaining consensus and driving action on policy recommendations across diverse stakeholder groups with potentially competing priorities.

Learning & Development Opportunities:

• Deep Dive into AI Ethics & Policy: Gain unparalleled expertise in the critical policy questions shaping the future of AI.

• Cutting-Edge Research Methods: Opportunity to innovate and apply novel quantitative methods to unique AI-related research problems.

• Influencing Global AI Standards: Contribute to shaping policies that could set precedents for AI deployment worldwide.

• Mentorship from AI Leaders: Learn from world-class researchers, engineers, and policy experts at the forefront of AI development.

📝 Enhancement Note: The challenges highlight the pioneering nature of this role, requiring adaptability, strategic thinking, and a proactive approach to problem-solving in an emerging field. The growth opportunities underscore the potential for significant professional development and impact.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you established a new measurement framework or defined key metrics for a complex product or policy. What was your process, and what was the impact?" (Focus on your ability to operationalize policy measurement.)

• "How would you approach understanding user concerns or behaviors related to a new AI policy, especially when there's limited prior data or research in this area?" (Demonstrate your approach to ambiguity and quantitative research design.)

Company & Culture Questions:

• "Why are you interested in applying quantitative UX research to product policy at OpenAI, specifically?" (Connect your skills and passion to OpenAI's mission and the role's challenges.)

• "How do you balance the need for rigorous data analysis with the urgency of policy development in a fast-paced environment?" (Highlight your adaptability and pragmatic approach.)

Portfolio Presentation Strategy:

• Narrative Arc: Structure your portfolio presentation around a compelling narrative for each project: the problem, your strategic approach, the data-driven solution, and the measurable outcome.

• Focus on "So What?": For every insight or finding, clearly articulate its implications for policy, product, or strategy. Don't just present data; explain its significance.

• Quantify Your Contribution: Use numbers to demonstrate the impact of your work wherever possible (e.g., "This insight led to a X% reduction in policy violations," or "Our metric framework enabled Y% faster identification of emerging issues").

• Address Ambiguity: Be prepared to discuss how you handled uncertainty, incomplete data, or unexpected results in your projects.

📝 Enhancement Note: Interview preparation should focus on demonstrating the practical application of quantitative research to policy problems, emphasizing metric definition, impact measurement, and cross-functional influence, all within the context of responsible AI development.

📌 Application Steps

To apply for this Quantitative UX Researcher, Product Policy position:

• Submit your application and resume through the OpenAI careers portal.

• Curate Your Portfolio: Select 3-5 of your most impactful quantitative research projects. Prioritize those that demonstrate your ability to tackle ambiguous problems, define and track metrics, and drive policy or product decisions. Ensure each project clearly outlines the problem, methodology, findings, and quantifiable impact.

• Tailor Your Resume: Highlight keywords and experiences directly relevant to quantitative research, user behavior analysis, policy development, data analysis, statistical modeling, and cross-functional collaboration. Quantify your achievements wherever possible.

• Prepare Your Presentation: Practice presenting one or two key portfolio projects. Focus on clear storytelling, emphasizing the "so what?" of your findings and the measurable impact of your work. Be ready to discuss your methodology and strategic choices.

• Research OpenAI's Mission: Understand OpenAI's commitment to responsible AI and its product policy challenges. Articulate how your skills and experience align with these goals and how you can contribute to their mission of benefiting humanity.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.