UX Researcher, Quantitative

📍 Job Overview

Job Title: UX Researcher, Quantitative

Company: Meta

Location: Bellevue, WA; Menlo Park, CA

Job Type: Full-Time

Category: User Experience Research / Product Operations

Date Posted: 2026-08-12

Experience Level: 8-13+ Years (depending on degree)

Remote Status: On-site

🚀 Role Summary

• Drive impactful product decisions through rigorous quantitative UX research methodologies and data-driven insights.

• Design and execute comprehensive primary research studies to understand user behavior and attitudes across Meta's global product portfolio.

• Act as a strategic partner to product, design, engineering, and marketing teams, translating complex research findings into actionable recommendations.

• Champion the voice of the user by advocating for human-centered design principles and ensuring product development aligns with user needs and societal impact.

• Leverage advanced statistical analysis and programming skills (R/Python) to uncover deep insights and predict user trends.

📝 Enhancement Note: This role is positioned within Meta's broader UX Research team, focusing specifically on quantitative methods to inform product strategy and development. The emphasis on "Quantitative" in the title, coupled with requirements for R/Python, statistical analysis, and survey design, strongly indicates a need for researchers who can derive measurable insights from large datasets and structured research initiatives, directly influencing product roadmaps and feature development.

📈 Primary Responsibilities

• Collaborate closely with cross-functional stakeholders (Product Management, Design, Engineering, Marketing, Content Strategy) to define research questions and identify key areas for investigation.

• Design and implement end-to-end primary research studies, employing a diverse range of quantitative methodologies such as large-scale surveys, experimental designs, and behavioral data analysis.

• Develop robust survey instruments, including questionnaire design, sampling strategies, and techniques to mitigate response bias, ensuring data integrity and validity.

• Apply advanced statistical analysis techniques, including regressions, ANOVA, and T-tests, to interpret complex datasets and extract meaningful user insights.

• Translate research findings into clear, compelling, and actionable strategic narratives, supported by data visualizations and impactful presentations for both technical and non-technical audiences.

• Act as a thought leader in quantitative UX research, continuously exploring and advocating for innovative research methods and tools to enhance product understanding.

• Manage multiple research projects simultaneously in fast-paced, ambiguous, and rapidly evolving environments, ensuring alignment with business objectives and timely delivery of critical insights.

• Generate insights that not only evaluate existing designs but also fuel ideation for new features and product innovations.

• Contribute to the responsible and ethical application of AI in research workflows, including risk assessment, bias mitigation, and quality assurance.

• Stay abreast of emerging AI technologies and techniques, such as prompt engineering and agent orchestration, to optimize research processes and drive measurable impact.

📝 Enhancement Note: The responsibilities highlight a blend of deep analytical work and strategic communication. The emphasis on "end-to-end custom primary research" and "design studies that address both user behavior and attitudes" indicates a need for researchers who can independently scope, execute, and deliver on complex research projects, rather than just executing pre-defined tasks. The inclusion of AI tool optimization suggests a forward-thinking approach to research operations.

🎓 Skills & Qualifications

Education:

• Bachelor's degree with 13+ years of relevant experience in user experience, applied research, and/or product research and development.

• OR Master's degree with 11+ years of relevant experience.

• OR PhD with 8+ years of relevant experience.

• Preferred degrees are in Human-Computer Interaction, Psychology, Sociology, Communication, Information Science, Media Studies, Computer Science, or Economics. Experience:

• Extensive experience in designing and executing quantitative user experience research studies.

• Proven track record of translating research findings into strategic narratives and actionable product recommendations.

• Demonstrated ability to work autonomously and manage research plans in ambiguous, fast-changing environments. Required Skills:

• Quantitative Research Design: Expertise in designing rigorous studies that effectively measure user behavior and attitudes.

• Statistical Analysis: Proficiency in applying statistical methods such as Regressions, ANOVA, and T-Tests to analyze research data.

• Survey Methodology: Deep understanding of survey design principles, sampling techniques, and response effects to ensure data accuracy and reliability.

• Programming Languages: Experience coding with R or Python for data analysis and research automation.

• Research Execution: Hands-on experience in executing primary research, from planning to analysis and reporting.

• Stakeholder Communication: Excellent ability to communicate complex research findings and strategic recommendations to diverse technical and non-technical audiences through presentations, reports, and data visualizations.

• Product Acumen: Ability to work cross-functionally with design, product management, engineering, and marketing teams to inform product development.

Preferred Skills:

• AI Integration: Demonstrated ability to integrate AI tools to optimize research workflows, drive efficiency gains, and improve research quality.

• Ethical AI Practices: Experience adhering to and implementing responsible AI practices, including risk assessment, bias mitigation, and quality reviews.

• AI Skill Development: Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.

• Consumer Insights: Experience with consumer products, consumer insights, or product development cycles.

• Data Visualization Tools: Proficiency in creating compelling data visualizations to communicate research findings effectively.

📝 Enhancement Note: The extensive experience requirement (13+ years with a Bachelor's) suggests this is a senior or lead role, requiring not just execution but also strategic direction and thought leadership within the quantitative research domain. The emphasis on AI skills indicates Meta's commitment to leveraging cutting-edge technology in their research operations.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Quantitative Study Designs: Showcase examples of well-designed quantitative research studies, clearly outlining objectives, methodologies, and participant recruitment strategies.

• Data Analysis & Insights: Present case studies demonstrating your ability to perform complex statistical analysis (e.g., regressions, ANOVA, T-tests) and derive actionable insights from quantitative data.

• Survey Instruments: Include examples of sophisticated survey questionnaires you have developed, highlighting your approach to question design, scaling, and bias mitigation.

• Programming Proficiency: Provide evidence of your R or Python coding skills, perhaps through code snippets or descriptions of analytical scripts used for research.

• Impactful Reporting: Demonstrate your ability to translate complex quantitative findings into clear, concise, and visually compelling reports and presentations tailored for diverse stakeholders.

Process Documentation:

• Research Planning & Execution: Document your systematic approach to planning and executing quantitative research projects from inception to completion, including project management strategies for ambiguous environments.

• Methodological Rigor: Detail your process for ensuring the methodological rigor and validity of quantitative studies, including sampling, data collection, and analysis protocols.

• Cross-Functional Collaboration: Illustrate your process for effectively collaborating with product, design, and engineering teams to ensure research is aligned with product goals and insights are integrated into the development lifecycle.

• AI Integration Workflow: Describe your process for incorporating AI tools into research workflows, focusing on efficiency, quality improvement, and ethical considerations.

📝 Enhancement Note: For a senior quantitative UX Researcher role at a company like Meta, a portfolio is crucial. It should not just show what was done, but how it was done, emphasizing the strategic thinking, methodological rigor, and the quantifiable impact of the research on product decisions. Demonstrating the ability to manage complex projects autonomously and integrate new technologies like AI will be key differentiators.

💵 Compensation & Benefits

Salary Range: $164,000/year to $227,000/year (USD)

Benefits:

• Bonus: Performance-based bonus opportunities.

• Equity: Stock options or grants, providing ownership and potential for long-term financial growth.

• Comprehensive Benefits Package: Includes health insurance (medical, dental, vision), retirement savings plans (e.g., 401k), paid time off (vacation, sick leave, holidays), parental leave, and potentially other wellness programs.

Working Hours:

• Standard full-time work schedule, typically 40 hours per week.

• While the role is on-site, Meta often offers flexibility in daily working hours, allowing for adjustments to accommodate personal needs, provided core business hours and stakeholder availability are met.

📝 Enhancement Note: The salary range provided is competitive for a senior quantitative UX Researcher in the specified locations (Bellevue, WA and Menlo Park, CA), reflecting the high cost of living and the specialized skill set required. The inclusion of bonus and equity highlights Meta's compensation structure for senior talent, emphasizing performance and long-term commitment. The "Benefits" are standard for large tech companies but are crucial for overall compensation.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Social Media, Internet Services, Virtual Reality, Artificial Intelligence)

Company Size: Large Enterprise (over 10,000 employees)

Founded: 2004

Company Slogan: "Meta builds technologies that help people connect, find communities and grow businesses."

Team Structure:

• Operations Team Aspect 1: The UX Research team at Meta is likely structured into pods or domains, with researchers embedded within specific product groups (e.g., Facebook, Instagram, Reality Labs, AI Research). Quantitative UX Researchers would form a specialized cohort within these product-aligned teams, potentially with a central research operations or community of practice.

• Operations Team Aspect 2: Researchers typically report to a Research Manager or Lead, who oversees research strategy and individual development within a product area or domain. There's a strong emphasis on autonomy and individual contribution at the senior level.

• Operations Team Aspect 3: Collaboration is a cornerstone, with researchers working daily alongside Product Managers, Designers, Engineers, Data Scientists, and other Research Scientists to define product direction and validate user experiences.

Methodology:

• Operations Process 1: Meta heavily relies on data-driven decision-making. Quantitative UX Research plays a critical role in generating the empirical evidence needed to support product strategies, A/B test hypotheses, and measure the impact of product changes.

• Operations Process 2: Research plans are often iterative and agile, adapting to the fast-paced product development cycles. This involves continuous research, rapid prototyping validation, and long-term foundational studies to understand user needs deeply.

• Operations Process 3: The company encourages innovation and experimentation. Researchers are expected to explore new methodologies, tools (including AI), and approaches to gain novel insights and push the boundaries of user understanding.

Company Website: https://www.metacareers.com/

📝 Enhancement Note: Meta's culture is characterized by rapid iteration, a data-first mindset, and a focus on large-scale impact. For a quantitative UX Researcher, this means working in a high-pressure, high-reward environment where research directly informs products used by billions. The emphasis on AI integration reflects Meta's strategic investment in this area across all functions.

📈 Career & Growth Analysis

Operations Career Level: Senior Quantitative UX Researcher (or equivalent, e.g., Research Scientist II/III, depending on internal leveling). This level signifies a high degree of autonomy, the ability to lead complex research initiatives, and a significant impact on product strategy. It requires deep expertise in quantitative methodologies and a strong understanding of product development lifecycles.

Reporting Structure: Typically reports to a Research Manager or Lead within a specific product group. The role involves close collaboration with senior leaders in Product Management, Design, and Engineering.

Operations Impact: Quantitative UX Researchers at Meta have a direct and substantial impact on the company's success. Their insights inform critical product decisions, feature development, platform strategy, and the overall user experience for billions of users globally. They help optimize engagement, retention, and user satisfaction through data-driven evidence.

Growth Opportunities:

• Operations Skill Advancement: Opportunities to deepen expertise in advanced statistical modeling, experimental design, causal inference, and specialized quantitative methods. Potential to transition into more specialized research roles (e.g., Causal Inference Researcher, Machine Learning Researcher).

• Leadership Development: Progression to Senior Manager of UX Research, Principal Researcher, or roles focused on research operations strategy and tooling. This might involve managing a team of researchers, setting research direction for a large product area, or contributing to Meta's research best practices.

• Cross-Functional Leadership: Opportunities to lead research initiatives that span multiple product teams or even major product pillars, requiring significant stakeholder management and strategic influence across different departments. Mentorship of junior researchers is also a common growth path.

📝 Enhancement Note: The career path at Meta for senior researchers emphasizes both deep specialization and broad strategic influence. The "10+" years of experience requirement points towards a role that is beyond individual contribution, expecting leadership in research strategy, methodology, and cross-functional collaboration.

🌐 Work Environment

Office Type: Modern, collaborative, and tech-forward office spaces designed to foster innovation and teamwork. The environment is typically open-plan with dedicated areas for focused work, team collaboration, and informal meetings.

Office Location(s):

• Bellevue, WA: Located in the Seattle metropolitan area, a major tech hub with a significant presence for Meta.

• Menlo Park, CA: Meta's global headquarters, situated in the heart of Silicon Valley, offering a vibrant and dynamic work environment.

Workspace Context:

• Workspace Aspect 1: The workspace is designed for collaboration, with numerous meeting rooms, huddle spaces, and common areas equipped with advanced AV technology to facilitate seamless communication, both in-person and remote.

• Workspace Aspect 2: Access to cutting-edge internal tools, software, and hardware for research, data analysis, and collaboration. This includes powerful computing resources, specialized research platforms, and comprehensive analytics suites.

• Workspace Aspect 3: Frequent opportunities for informal and formal interactions with a diverse range of talented professionals across product, design, engineering, and research, fostering a rich learning and networking environment.

Work Schedule: While the role is designated as on-site, Meta is known for offering a degree of flexibility within the standard work week to accommodate personal needs, as long as core responsibilities and team collaboration requirements are met. This allows researchers to manage their schedules effectively while ensuring availability for critical team syncs and stakeholder engagements.

📝 Enhancement Note: The on-site requirement at these major tech hubs means candidates should be prepared for a highly collaborative and fast-paced office environment. The expectation is active participation in team activities and in-person collaboration, balanced with the flexibility often afforded by large tech companies.

📄 Application & Portfolio Review Process

Interview Process:

• Process Step 1 (Recruiter Screen): Initial call to assess basic qualifications, experience alignment, and understanding of the role. Candidates should be prepared to articulate their quantitative research experience and interest in Meta.

• Process Step 2 (Hiring Manager Interview): Deeper dive into experience, research philosophy, and problem-solving approaches.

Focus on quantitative methodologies, statistical expertise, and previous impact.

• Process Step 3 (Technical/Research Interviews): Typically 2-4 interviews with peer researchers and/or cross-functional partners. These will include:

  • Quantitative Study Design: Presenting a hypothetical research problem and designing a quantitative study to address it.
  • Data Analysis & Interpretation: Discussing past projects, explaining analytical choices (R/Python, stats), and interpreting simulated or past data.
  • Stakeholder Management & Communication: Behavioral questions about collaborating with diverse teams and communicating complex findings.
  • AI/Tooling: Discussion on how you've used or plan to use AI in your research workflows.

• Process Step 4 (Final Interview/Debrief): Often a conversation with a senior leader or a debrief session to synthesize feedback and make a final hiring decision.

Portfolio Review Tips:

• Focus on Impact: Clearly articulate the business or product impact of your research. Quantify results wherever possible (e.g., "led to a X% increase in conversion," "informed a feature that reduced support tickets by Y%").

• Methodological Depth: For each project, detail the quantitative methods used, your rationale for choosing them, and how you ensured rigor and validity. Showcase your R/Python skills and statistical analysis applications.

• Problem-Solution Structure: Present your projects using a clear problem-solution framework: What was the problem? What research questions did you ask? How did you design your study? What were the findings? What were the recommendations? What was the outcome?

• AI Integration Examples: If possible, include a project where you leveraged AI tools to streamline research, enhance analysis, or gain new insights.

• Tailor to Quantitative: Ensure your portfolio strongly emphasizes your quantitative skills, statistical capabilities, and ability to handle complex datasets.

Challenge Preparation:

• Hypothetical Study Design: Be ready to design a quantitative study for a given product scenario. Think about target populations, sampling, key metrics, survey questions, and statistical tests.

• Data Interpretation Scenarios: Practice interpreting charts, statistical outputs (e.g., regression tables), and drawing conclusions.

• Behavioral Questions: Prepare STAR (Situation, Task, Action, Result) method responses for questions about collaboration, conflict resolution, managing ambiguity, and influencing stakeholders.

• AI in Research: Be prepared to discuss your understanding of AI's role in UX research, including its benefits, limitations, and ethical considerations.

📝 Enhancement Note: The interview process at Meta is rigorous and designed to assess deep technical expertise, strategic thinking, and collaborative capabilities. A strong portfolio showcasing quantitative prowess and measurable impact is essential for success. Preparing for hypothetical study design and data interpretation scenarios is critical.

🛠 Tools & Technology Stack

Primary Tools:

• Statistical Software: R and Python are explicitly required for data analysis. This implies proficiency with libraries such as dplyr, ggplot2, stats in R, and pandas, numpy, scipy, matplotlib, seaborn in Python.

• Survey Platforms: Experience with enterprise-grade survey tools (e.g., Qualtrics, SurveyMonkey Enterprise, internal Meta tools) for designing, deploying, and managing large-scale surveys.

• Experimental Design Tools: Familiarity with platforms or methodologies for designing and analyzing A/B tests and other controlled experiments.

Analytics & Reporting:

• Data Visualization Tools: Proficiency in tools like Tableau, Power BI, or Python/R visualization libraries (e.g., ggplot2, matplotlib, seaborn) to create compelling dashboards and reports.

• Web Analytics: Experience with web analytics platforms (e.g., Google Analytics, Adobe Analytics, internal Meta analytics tools) for understanding user behavior on digital products.

• Database Querying: Potentially SQL for extracting and manipulating data from large databases.

CRM & Automation:

• While not directly a CRM role, understanding how user data is managed and how research insights feed into product roadmaps (often managed via product management tools like Jira, Asana, or internal equivalents) is beneficial.

• AI & Machine Learning Tools: Familiarity with AI platforms and tools that can assist in data analysis, pattern recognition, survey analysis, or even generative AI for research synthesis.

📝 Enhancement Note: The explicit mention of R and Python for statistical analysis is a key technical requirement. Candidates should be prepared to demonstrate their proficiency with these tools, including relevant libraries for data manipulation, statistical modeling, and visualization. Experience with enterprise survey platforms and data visualization tools is also critical for this role.

👥 Team Culture & Values

Operations Values:

• Data-Driven Decision Making: A core value at Meta. Quantitative UX Researchers are expected to provide robust, empirical evidence to support product decisions and strategies.

• User Empathy & Advocacy: While quantitative, the research still serves to understand and advocate for users. The data should tell a human story about needs, pain points, and desires.

• Collaboration & Transparency: Working effectively across diverse teams and sharing findings openly to drive collective understanding and progress.

• Innovation & Experimentation: A culture that encourages trying new methods, tools (especially AI), and approaches to uncover deeper insights and solve complex problems.

• Impact & Scale: A focus on delivering research that has a significant, measurable impact on products used by billions of people globally.

Collaboration Style:

• Cross-Functional Integration: Researchers are deeply embedded within product teams, working hand-in-hand with Product Managers, Designers, and Engineers on a daily basis.

• Process Review & Feedback: A culture of constructive feedback is common, with peer reviews of research plans, methodologies, and findings to ensure quality and rigor.

• Knowledge Sharing: Active participation in internal research communities, sharing best practices, lessons learned, and emerging trends through presentations, internal forums, and documentation.

📝 Enhancement Note: The values emphasize a blend of analytical rigor, user advocacy, and collaborative innovation. Quantitative researchers are expected to be not just analysts but also strategic partners who can translate data into compelling narratives that drive product direction within a large, fast-paced organization.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity: Operating in a fast-paced, rapidly evolving environment with shifting priorities requires adaptability and strong project management skills.

• Driving Adoption of Insights: Ensuring that complex quantitative findings are understood, trusted, and acted upon by diverse stakeholder groups, especially those less familiar with statistical analysis.

• Ethical AI Implementation: Staying ahead of the curve on responsible AI practices, mitigating bias, and ensuring data privacy and security when integrating AI into research workflows.

• Measuring Long-Term Impact: Demonstrating the ROI of quantitative research over extended product development cycles and linking research efforts to sustained business outcomes.

Learning & Development Opportunities:

• Operations Skill Advancement: Access to internal training, workshops, and mentorship programs focused on advanced statistical techniques, causal inference, experimental design, and emerging AI/ML methodologies relevant to research.

• Industry Exposure: Opportunities to attend leading industry conferences (e.g., CHI, UXRConf, KDD) and potentially contribute through presentations or publications.

• Mentorship & Leadership: Opportunities to mentor junior researchers, lead research initiatives, and develop leadership skills through formal training and hands-on experience, paving the way for Principal Researcher or Management roles.

📝 Enhancement Note: The challenges presented are typical for senior roles in large tech companies, requiring not only technical expertise but also strong soft skills in communication, influence, and adaptability. The growth opportunities are robust, offering clear paths for both deep technical specialization and leadership development within the research domain.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex quantitative research project you led from conception to completion. What was the business problem, what methods did you use, what were your key findings, and what was the ultimate impact on the product?" (Focus on R/Python, stats, impact)

• "How would you design a study to measure the impact of a new AI-powered feature on user engagement, considering potential biases and ethical implications?" (Assess design thinking, AI awareness, quantitative rigor)

• "Imagine you've found statistically significant but counter-intuitive results. How would you communicate these findings to a product team that is heavily invested in a different direction?" (Evaluate stakeholder management, communication strategy, data storytelling) Company & Culture Questions:

• "Why are you interested in quantitative UX research at Meta specifically, and how do you see your skills contributing to our mission?" (Assess motivation, alignment with Meta's values and products)

• "Describe a time you had to collaborate with engineers or product managers who were skeptical of your research findings. How did you build trust and influence their decisions?" (Evaluate collaboration style, influence, and resilience)

• "How do you stay current with the latest advancements in quantitative research methodologies and AI technologies?" (Assess commitment to continuous learning and industry awareness) Portfolio Presentation Strategy:

• Structure Your Narrative: For each project, clearly define the problem, your approach (methodology, tools, stats), your key findings, and the resulting impact. Use a consistent structure.

• Show, Don't Just Tell: Be prepared to walk through specific examples of your survey design, code snippets (if applicable and allowed), statistical outputs, and data visualizations.

• Quantify Everything: Emphasize the measurable outcomes of your research. If you can't quantify directly, discuss the qualitative impact and how it informed concrete product decisions.

• Highlight AI Integration: If you have relevant experience, be ready to discuss how you've used AI tools to optimize research processes, improve efficiency, or derive new insights.

• Be Ready for Deep Dives: Interviewers may ask probing questions about your methodological choices, statistical reasoning, and how you handled challenges.

📝 Enhancement Note: Interview preparation should focus on demonstrating deep quantitative expertise, strategic thinking, and the ability to translate complex data into actionable insights that drive product success at scale. Being prepared to discuss AI integration and ethical considerations is increasingly important.

📌 Application Steps

To apply for this Quantitative UX Researcher position at Meta:

• Submit your application through the official Meta Careers portal using the provided URL.

• Tailor your resume: Highlight your extensive experience (8-13+ years) in quantitative UX research, specifically mentioning your proficiency with R/Python, statistical analysis (Regressions, ANOVA, T-Tests), and survey design. Quantify your achievements and impact wherever possible.

• Prepare your portfolio: Curate 2-3 strong case studies that showcase your end-to-end quantitative research process, from study design and data analysis to actionable insights and measurable product impact. Ensure it highlights your AI integration experience if applicable.

• Practice your interview responses: Prepare for behavioral questions using the STAR method and be ready to walk through hypothetical study designs and data interpretation scenarios. Rehearse articulating your research strategy and findings clearly and concisely.

• Research Meta's products and values: Understand Meta's mission, its diverse product ecosystem, and its commitment to data-driven decision-making and ethical AI. Be prepared to discuss how your work aligns with these aspects.

⚠️ 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.