Quantitative UX Researcher

📍 Job Overview

Job Title: Quantitative UX Researcher

Company: OpenAI

Location: San Francisco, California, United States

Job Type: Full-Time

Category: User Experience Research / Data Science

Date Posted: February 09, 2026

Experience Level: 5+ Years

Remote Status: Hybrid (3 days in office per week)

🚀 Role Summary

• Spearhead quantitative UX research initiatives to drive product innovation and ensure responsible AI development.

• Design and execute rigorous studies utilizing surveys, experiments, and behavioral data analysis to inform critical product decisions.

• Collaborate closely with product, design, engineering, and data science teams to translate complex data into actionable insights and strategic recommendations.

• Apply advanced statistical analysis and survey methodology to ensure research findings are valid, reliable, and decision-ready.

• Influence product direction through compelling data-driven storytelling and effective stakeholder management.

📝 Enhancement Note: This role is positioned within a small but high-impact UX Research team at OpenAI, emphasizing the need for individuals who can operate autonomously and drive significant influence. The focus on "responsible AI" and "human needs" signals a mission-driven environment where research directly contributes to ethical product development.

📈 Primary Responsibilities

• Design and execute quantitative research studies throughout the entire product lifecycle, from initial concept validation to prototyping and usability evaluations.

• Partner with Product Managers, Designers, Engineers, and Data Scientists to pinpoint high-impact opportunities where quantitative insights can significantly shape product strategy and roadmap decisions.

• Lead the development and deployment of customized research methodologies, including sophisticated surveys, controlled experiments, and in-depth behavioral data analysis.

• Synthesize complex quantitative findings into clear, concise, and actionable recommendations that foster cross-functional alignment and drive effective decision-making.

• Ensure the validity and reliability of research results through the rigorous application of statistical principles and best practices in survey design and execution.

• Articulate research findings and their implications to senior leadership and diverse stakeholders, effectively influencing product direction and advocating for user needs.

• Contribute to the establishment and refinement of best practices for quantitative UX research within a rapidly evolving AI landscape.

📝 Enhancement Note: The responsibilities highlight a blend of strategic thinking and tactical execution. The emphasis on "customized studies" and "translating complex data" suggests a need for researchers who can adapt methodologies to unique AI product challenges and communicate findings effectively to both technical and non-technical audiences.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in a quantitative field such as Psychology, Statistics, Computer Science, Human-Computer Interaction (HCI), Data Science, Economics, or a related discipline is highly recommended.

Experience: 5+ years of progressive experience in user experience research, market research, data science, or a closely related field, with a demonstrable specialization in quantitative methodologies.

Required Skills:

• Deep expertise in quantitative research methodologies, including experimental design, survey design and administration, statistical analysis, and behavioral data analysis.

• Proficiency in data analysis tools and programming languages such as SQL and Python.

• Exceptional written and verbal communication skills, with a proven ability to translate complex data into clear, actionable insights and influence decision-making at all organizational levels.

• Demonstrated experience leading end-to-end quantitative research projects that have had a significant and lasting impact on product strategy and design.

• Experience effectively collaborating with and presenting to senior stakeholders and executive leadership.

Preferred Skills:

• Experience with A/B testing frameworks and causal inference techniques.

• Familiarity with machine learning concepts and their application in user behavior analysis.

• Experience in the AI or technology sector, particularly with complex or novel product categories.

• Experience working in a fast-paced, research-intensive environment.

• Familiarity with qualitative research methods to complement quantitative findings.

📝 Enhancement Note: The minimum experience requirement of "5+ years" suggests this is not an entry-level role. The emphasis on "senior stakeholders" and "raising the bar" indicates a need for seasoned professionals who can operate with a high degree of autonomy and strategic foresight. The inclusion of SQL and Python points towards a hands-on role requiring data manipulation and analytical skills.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase at least 2-3 comprehensive case studies detailing quantitative research projects from inception to impact.

• Clearly articulate the research problem, the chosen quantitative methodologies (e.g., survey design, experimental setup, data analysis techniques), and the rationale behind these choices.

• Present data visualizations and key findings in a clear, compelling manner, demonstrating an ability to derive actionable insights from complex datasets.

• Detail the impact of your research on product decisions, user experience improvements, or business outcomes, quantifying results where possible (e.g., through metrics like conversion rate improvements, satisfaction score increases, or reduction in usability issues).

Process Documentation:

• Demonstrate a systematic approach to research planning, including defining research objectives, identifying key metrics, and outlining study designs.

• Provide examples of how you have documented survey instruments, experimental protocols, and data analysis plans to ensure rigor and reproducibility.

• Showcase experience in data cleaning, validation, and analysis, outlining your workflow for ensuring data integrity.

• Illustrate how you have translated raw data into compelling narratives and actionable recommendations for product teams.

📝 Enhancement Note: For a Quantitative UX Researcher role, the portfolio is critical for demonstrating practical application of skills. Emphasis should be placed on the rigor of the quantitative methods used and the impact of the findings. Case studies should clearly delineate the research process, analytical approach, and measurable outcomes.

💵 Compensation & Benefits

Salary Range: $266,000 - $385,000 USD per year.

Benefits:

• Comprehensive health, dental, and vision insurance.

• Generous paid time off and holidays.

• 401(k) plan with company match.

• Stock options or equity grants.

• Relocation assistance.

• Professional development opportunities and support for continuous learning.

• Access to cutting-edge AI research and development.

• Opportunities to contribute to high-impact, world-changing technology.

Working Hours: This is a full-time position, typically requiring approximately 40 hours per week. While a hybrid model is in place, flexibility may be offered based on project needs and team collaboration, adhering to standard business hours in the San Francisco region.

📝 Enhancement Note: The provided salary range is highly competitive, reflecting the seniority of the role and OpenAI's position in the tech industry. The benefits package is standard for a leading technology company, with a particular emphasis on professional development and the unique opportunity to work on advanced AI.

🎯 Team & Company Context

🏢 Company Culture

Industry: Artificial Intelligence Research & Deployment. OpenAI is at the forefront of AI innovation, developing advanced AI systems and products aimed at benefiting humanity.

Company Size: OpenAI is a rapidly growing organization, with a significant number of employees (exact number not specified but implies a scale-up phase). This means a dynamic environment with evolving processes and a strong emphasis on innovation.

Founded: OpenAI was founded in 2015. Its mission is to ensure that artificial general intelligence benefits all of humanity.

Team Structure:

• The UX Research team is described as "small" but with "outsized impact," suggesting a lean and highly effective group.

• Researchers operate at the "intersection of users, product, and safety," indicating a broad scope of influence.

Methodology:

• The team focuses on bringing "clarity to complex questions" in fast-moving AI spaces.

• Research is used to "ensure powerful AI systems are built responsibly and grounded in real human needs."

• Quantitative methods are central to understanding users and informing "critical product decisions."

Company Website: https://openai.com/

📝 Enhancement Note: OpenAI's culture is characterized by a strong mission-driven focus, a commitment to safety and responsibility in AI development, and a fast-paced, innovative environment. The UX Research team's position underscores the company's dedication to user-centricity and ethical AI deployment.

📈 Career & Growth Analysis

Operations Career Level: This is a senior-level Quantitative UX Researcher position, requiring significant independent work and a proven track record of influencing product strategy. The role demands expertise in advanced quantitative methods and the ability to lead complex research initiatives.

Reporting Structure: The UX Research team is small, suggesting a flatter hierarchy. The role likely reports to a lead researcher or a director within the product or research organization. Close collaboration with Directors and VPs of Product, Engineering, and Design is expected.

Operations Impact: The Quantitative UX Researcher will have a direct impact on the development and refinement of OpenAI's AI products. By providing rigorous, data-driven insights into user needs, behaviors, and perceptions, the role will influence product direction, enhance user experience, and ensure the responsible and safe deployment of AI technologies. This impact extends to critical product decisions and the overall success of OpenAI's mission.

Growth Opportunities:

• Specialization: Deepen expertise in specific quantitative methods or emerging areas within AI user research.

• Leadership: Potentially move into a lead researcher or management role as the UX Research team expands.

• Cross-functional Influence: Expand influence across different product lines and strategic initiatives within OpenAI.

• Industry Contribution: Contribute to the evolving field of AI UX research through internal best practices and potentially external thought leadership.

• Skill Development: Opportunities to learn about cutting-edge AI technologies and their user implications, and to hone skills in advanced statistical analysis and data science.

📝 Enhancement Note: The growth trajectory for this role is towards becoming a subject matter expert and a key influencer in AI product development. The "small team, outsized impact" dynamic suggests that individuals who excel can rapidly gain visibility and responsibility.

🌐 Work Environment

Office Type: This role operates under a hybrid work model, requiring 3 days per week in the San Francisco office. This suggests a collaborative office environment designed to foster team interaction and in-person brainstorming, balanced with the flexibility of remote work.

Office Location(s): San Francisco, California, United States. This location places the researcher within a major tech hub, with access to talent and resources.

Workspace Context:

• The office environment likely supports collaboration through shared spaces, meeting rooms, and potentially dedicated research labs or equipment.

• Access to OpenAI's advanced technological infrastructure and data resources will be a key aspect of the workspace.

• Opportunities for frequent interaction with product, design, engineering, and data science teams will be integral to the daily workflow.

Work Schedule: A standard full-time schedule (approx. 40 hours/week) is expected, with a hybrid arrangement. While core hours are likely, the nature of research and product development may require occasional flexibility to meet project deadlines or accommodate global team members if applicable.

📝 Enhancement Note: The hybrid model at OpenAI emphasizes intentional collaboration, suggesting that office days will be focused on teamwork, meetings, and in-person discussions, while remote days allow for focused individual work and data analysis.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review your application and resume, focusing on alignment with the core requirements and experience.

• Hiring Manager Interview: An in-depth discussion with the hiring manager to assess your experience, research philosophy, and fit with the team. Expect questions about your quantitative research background and specific project examples.

• Technical/Skills Interview: This stage will likely involve a deep dive into your quantitative skills. You may be asked to discuss your experience with SQL, Python, statistical modeling, experimental design, and survey methodology. This could involve theoretical questions or a take-home assignment.

• Portfolio Presentation: A critical step where you will present 1-2 detailed case studies from your portfolio. Focus on clearly articulating the problem, your methodology, your findings, and the impact of your work. Be prepared to answer detailed questions about your process and decision-making.

• Cross-functional Interviews: Interviews with potential collaborators (e.g., Product Managers, Designers, Engineers, Data Scientists) to assess your ability to work effectively in a cross-functional team and influence product decisions.

• Final Round/Executive Interview: A final discussion, potentially with senior leadership, to assess overall fit, strategic thinking, and alignment with OpenAI's mission and values.

Portfolio Review Tips:

• Highlight Impact: Clearly demonstrate the tangible impact of your research on product decisions, user experience, or business metrics. Quantify results whenever possible.

• Showcase Rigor: Detail your quantitative methodologies, statistical approaches, and how you ensured the validity and reliability of your findings.

• Tell a Story: Structure your case studies as compelling narratives, guiding the reviewer through the problem, your process, and the outcome.

• Tailor to the Role: Emphasize projects that align with the responsibilities of a Quantitative UX Researcher at OpenAI, particularly those involving complex data, experimental design, or influencing product strategy.

• Be Prepared to Discuss: Anticipate detailed questions about your methodologies, data analysis choices, and how you handled challenges or ambiguity.

Challenge Preparation:

• Quantitative Analysis: Be ready to discuss statistical concepts, experimental design principles, and how you would approach analyzing specific types of user data.

• Problem Solving: Prepare to tackle hypothetical research problems related to AI products, outlining your approach and the quantitative methods you would employ.

• Communication: Practice articulating complex quantitative findings in a clear, concise, and persuasive manner, suitable for both technical and non-technical audiences.

📝 Enhancement Note: The interview process is designed to thoroughly vet candidates on their quantitative expertise, strategic thinking, and ability to drive impact. The portfolio presentation is a key component, demanding candidates to showcase not just what they did, but why they did it and the results achieved.

🛠 Tools & Technology Stack

Primary Tools:

• Statistical Software/Languages: Proficiency in Python (with libraries like Pandas, NumPy, SciPy, Statsmodels) and R is highly desirable for advanced statistical analysis and data manipulation.

• Database Querying: Strong skills in SQL for extracting and manipulating data from relational databases are essential.

• Survey Platforms: Experience with professional survey tools (e.g., Qualtrics, SurveyMonkey Enterprise, Typeform) for designing, deploying, and managing complex surveys.

• Experimentation Platforms: Familiarity with A/B testing tools and frameworks for designing and analyzing experiments.

Analytics & Reporting:

• Data Visualization Tools: Experience with tools like Tableau, Power BI, or Python visualization libraries (Matplotlib, Seaborn, Plotly) to create clear and impactful data visualizations.

• Spreadsheet Software: Advanced proficiency in Excel or Google Sheets for data manipulation and basic analysis.

• Business Intelligence Tools: Familiarity with BI platforms for creating dashboards and reports.

CRM & Automation:

• While not the primary focus, understanding how user data integrates with CRM systems (e.g., Salesforce) and automation platforms can be beneficial for context.

📝 Enhancement Note: The emphasis on Python, SQL, and statistical rigor indicates a need for a researcher who can function as a data analyst as well as a traditional UX researcher. The ability to work with large datasets and perform complex statistical analyses is paramount.

👥 Team Culture & Values

Operations Values:

• Mission-Driven: A strong commitment to OpenAI's mission of ensuring AI benefits all of humanity.

• Excellence & Rigor: A dedication to high-quality, methodologically sound research that raises the bar for the organization.

• Responsibility & Safety: A core focus on building AI systems that are safe, ethical, and grounded in human needs.

• Collaboration: A belief in the power of cross-functional teamwork to solve complex problems.

• Innovation: A drive to explore new frontiers in AI and user understanding in a rapidly evolving field.

Collaboration Style:

• Cross-functional Partnership: The team actively partners with Product, Design, Engineering, and Data Science, requiring open communication and mutual respect.

• Data-Driven Influence: Collaboration is based on presenting clear, data-backed insights to influence decisions and drive consensus.

• Mentorship & Learning: As a small team, there's an implicit value placed on sharing knowledge and supporting each other's growth.

• Iterative Feedback: A culture that encourages constructive feedback on research plans, methodologies, and findings to continuously improve the quality of work.

📝 Enhancement Note: The values emphasize a combination of technical excellence, ethical considerations, and collaborative problem-solving, all within the context of cutting-edge AI development.

⚡ Challenges & Growth Opportunities

Challenges:

• Pace of Innovation: The rapidly evolving nature of AI and the tech industry requires continuous learning and adaptation of research methods.

• Defining Norms: Working in a relatively new field means often defining best practices for quantitative UX research in AI, requiring creativity and initiative.

• Complexity of AI Systems: Understanding and researching user interactions with complex AI models can be challenging, requiring strong analytical and problem-solving skills.

• Balancing Stakeholder Needs: Effectively managing the diverse needs and expectations of various stakeholders (product, engineering, safety, leadership) requires strong communication and negotiation skills.

• Data Privacy & Ethics: Navigating the ethical considerations and data privacy implications inherent in AI research is a critical challenge.

Learning & Development Opportunities:

• Cutting-Edge Research: Direct exposure to and involvement in groundbreaking AI research and development.

• Advanced Methodologies: Opportunities to deepen expertise in statistical modeling, experimental design, and novel quantitative research techniques.

• Cross-Disciplinary Learning: Gaining exposure to areas like machine learning, AI safety, and product strategy from world-class experts.

• Professional Development: Support for attending conferences, workshops, or pursuing further education relevant to AI and UX research.

• Impactful Projects: The chance to contribute to projects with the potential for significant global impact.

📝 Enhancement Note: This role offers the challenge of working at the forefront of AI, providing immense growth opportunities for researchers eager to define the future of human-AI interaction.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used quantitative research to significantly influence a product decision. What was the problem, your methodology, the key findings, and the outcome?" (Focus on impact and your decision-making process.)

• "How would you design an experiment to test the usability or effectiveness of a new AI-powered feature? What metrics would you track, and how would you analyze the results?" (Demonstrate your experimental design and analytical thinking.)

Company & Culture Questions:

• "Why are you interested in working at OpenAI, and specifically in this Quantitative UX Researcher role?" (Connect your passion for AI and user research with OpenAI's mission.)

• "How do you approach collaborating with engineers, product managers, and designers who may have different priorities or levels of understanding regarding research?" (Highlight your communication and influencing skills.)

Portfolio Presentation Strategy:

• Structure: Begin with a clear problem statement, then detail your methodology, present key findings with supporting data visualizations, and conclude with actionable recommendations and demonstrated impact.

• Quantify Everything: Wherever possible, use numbers and metrics to illustrate the scope of your work and its outcomes.

• Explain Your 'Why': Be prepared to justify your methodological choices, especially when dealing with complex data or ambiguous situations.

• Focus on Impact: Clearly articulate how your research led to tangible improvements or informed critical decisions.

• Be Ready for Deep Dives: Anticipate detailed questions about your data analysis, statistical assumptions, and any challenges you faced.

📝 Enhancement Note: Preparation should focus on demonstrating a deep understanding of quantitative methods, a strategic approach to research, and the ability to translate complex data into actionable insights that drive product success.

📌 Application Steps

To apply for this Quantitative UX Researcher position:

• Submit your application through the provided link on Ashby.

• Portfolio Customization: Curate your portfolio to prominently feature 2-3 strong case studies that exemplify your quantitative research expertise, focusing on impact and methodological rigor. Tailor your CV to highlight experience with SQL, Python, experimental design, and survey methodology.

• Resume Optimization: Ensure your resume clearly outlines your experience in quantitative UX research, market research, or data science, emphasizing achievements and quantifiable results. Use keywords found in the job description such as "quantitative research," "experimental design," "statistical analysis," and "SQL/Python."

• Interview Preparation: Practice articulating your research process, methodologies, and the impact of your work. Prepare specific examples for common interview questions, especially those related to influencing product decisions and collaborating cross-functionally.

• Company Research: Familiarize yourself with OpenAI's mission, products, and recent developments. Understand their commitment to AI safety and responsibility, and consider how your quantitative research skills can contribute to these areas.

⚠️ 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.