Quantitative UX Researcher

Google
Full-time•São Paulo, São Paulo, Brazil
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📍 Job Overview

Job Title: Quantitative UX Researcher

Company: Google

Location: São Paulo, São Paulo, Brazil

Job Type: Full-time

Category: UX Research (Quantitative)

Date Posted: June 10, 2025

Experience Level: Mid-Senior Level (5-10 years estimated)

Remote Status: On-site

🎨 Role Summary

  • Drive impactful user experience improvements through empirical quantitative research methods.
  • Utilize advanced statistical analysis and programming skills to understand user behavior and needs.
  • Collaborate closely with cross-functional teams including UX Designers, Product Managers, and Engineers.
  • Translate complex data findings into actionable insights that inform product strategy and design decisions.
  • Contribute to the growth and knowledge sharing within Google's Quantitative UXR community.
📝 Enhancement Note: Based on the required 4 years of experience and the preference for advanced degrees and leadership experience, this role is likely positioned at a Mid-Senior level within Google's UX Research hierarchy. The responsibilities indicate a need for independent research execution and strategic influence.

🖼️ Primary Responsibilities

  • Scope, design, and execute comprehensive quantitative research studies, primarily leveraging surveys and logs analysis.
  • Develop and test hypotheses derived from existing user data to uncover behavioral patterns and insights.
  • Apply a range of quantitative methods, including crafting robust surveys, performing statistical analyses using Python/R, and developing custom code/models.
  • Define and measure key quantitative UX goals and metrics in close collaboration with partners across Design, Research, Data Science, and Engineering teams.
  • Work collaboratively with cross-functional stakeholders to identify, prioritize, and execute high-impact research opportunities that address critical product questions.
  • Analyze, synthesize, and effectively communicate complex research findings to diverse audiences, ensuring they are actionable for both technical experts and non-experts.
  • Translate data-driven insights into tangible recommendations that directly contribute to improving user experience and achieving business objectives.
📝 Enhancement Note: The responsibilities highlight a strong emphasis on independent research execution from scoping to communication, requiring a solid foundation in statistical methods, programming, and cross-functional collaboration. This aligns with expectations for a quantitative researcher capable of leading projects.

🎓 Skills & Qualifications

Education: Bachelor's degree in a relevant field or equivalent practical experience is required. A Master's degree or PhD in Computer Science, Human-Computer Interaction (HCI), Statistics, or a related quantitative field is strongly preferred, indicating a desire for advanced theoretical and practical knowledge in research methodologies.

Experience: A minimum of 4 years of experience in an applied research setting or a similar quantitative role is required. Preferred experience includes conducting UX research specifically on products, working with executive stakeholders (Director level and above), and leading research projects that have demonstrably impacted product ideas, strategy, and business improvements. Experience with portfolio cases demonstrating the application of quantitative methods to real-world product challenges is highly valued.

Required Skills:

  • Proficiency in programming languages commonly used for data manipulation and computational statistics, such as Python, R, MATLAB, C++, Java, or Go.
  • Proven experience with survey design principles and the development of robust quantitative metrics for measuring user experience and behavior.
  • Solid understanding and experience in applied quantitative research methodologies, including designing and executing studies using logs analysis and survey data.
  • Ability to analyze and synthesize complex quantitative data to derive meaningful insights about user behavior and needs.

Preferred Skills:

  • Experience in programming computational and statistical algorithms specifically for analyzing large datasets, coupled with knowledge of technical analysis tools within relevant domains.
  • Demonstrated experience in descriptive, inferential, and multivariate statistics, as well as a strong understanding of experimental design principles.
  • Proven ability to communicate complex research findings effectively through compelling deliverables tailored to various audiences, and to build strong collaborative relationships with cross-functional partners.
  • Experience with advanced quantitative methods such as path modeling and regression analysis.
📝 Enhancement Note: The combination of required programming skills, statistical knowledge, and preferred advanced degrees suggests a role that demands both strong theoretical understanding of quantitative research and practical ability to apply it using code and statistical tools. The preference for leadership experience indicates this role may involve mentoring or project leadership.

🎨 Portfolio & Creative Requirements

Portfolio Essentials:

  • Include case studies that clearly demonstrate your quantitative research process from problem definition and hypothesis generation to data collection, analysis, and insight delivery.
  • Showcase projects where you have utilized programming languages (Python, R, etc.) for data manipulation, statistical analysis, and model development.
  • Present examples of survey designs you have created, explaining the rationale behind question wording, structure, and sampling methods.
  • Highlight projects where your quantitative research findings directly influenced product decisions or led to measurable improvements in user experience or business outcomes.

Process Documentation:

  • Document your research methodology, explaining the statistical techniques applied, the rationale for choosing specific methods, and any assumptions made.
  • Detail your approach to data cleaning, processing, and preparation, especially when working with large datasets like logs analysis.
  • Showcase your process for translating raw data and statistical outputs into clear, concise, and actionable insights for non-technical stakeholders.
📝 Enhancement Note: For a Quantitative UX Researcher role, the portfolio should heavily emphasize the rigor of the research methodology, the technical skills in data analysis and programming, and the ability to translate quantitative findings into tangible product impact. Case studies should function as detailed walkthroughs of the research process.

💵 Compensation & Benefits

Salary Range: While a specific salary range is not provided in the job description, based on typical compensation for a Mid-Senior level Quantitative UX Researcher at a major tech company like Google in São Paulo, Brazil, the estimated annual salary range could be between R$ 180,000 and R$ 300,000+. This estimate is based on publicly available salary data for similar roles in São Paulo and takes into account Google's position as a leading global technology company. Local cost of living and market demand for quantitative research skills in the Brazilian tech sector have also been considered. Candidates are encouraged to discuss compensation expectations during the interview process.

Benefits:

  • Comprehensive health and dental insurance plans.
  • Retirement savings plans with company match.
  • Generous paid time off, including vacation and sick leave.
  • Professional development opportunities, including access to training programs and internal research tools.
  • Potential for performance-based bonuses and equity packages.
  • Wellness programs and employee assistance programs.

Working Hours: This is a full-time, on-site position, typically involving standard business hours (e.g., 40 hours per week). Flexibility may be available depending on team needs and project timelines, but the core expectation is presence in the São Paulo office.

📝 Enhancement Note: The salary estimate is an inference based on market data for similar roles at large tech companies in São Paulo, Brazil. It considers the reported experience level and the specific technical and analytical skills required. Candidates should use this as a general guideline and seek clarification during the interview process.

🎯Team & Company Context

🏢 Company & Design Culture

Industry: Google operates within the broad technology and internet services industry, with this specific role likely supporting products within their core offerings or emerging areas. The industry is characterized by rapid innovation, data-driven decision-making, and a strong focus on user experience to maintain market leadership.

Company Size: Google is a very large global organization with over 10,001 employees. This size implies a complex organizational structure, numerous product teams, and extensive resources for research and development. For a Quantitative UX Researcher, this means access to vast datasets and opportunities to work on products with massive user bases.

Founded: Google was founded in 1998. Its long history is marked by a culture of innovation, engineering excellence, and a strong emphasis on data and research to inform product development.

Team Structure:

  • The UX team at Google is multi-disciplinary, comprising Designers, Researchers, Writers, Content Strategists, Program Managers, and Engineers.
  • Quantitative UX Researchers collaborate closely with other UX disciplines, as well as Product Managers, Data Scientists, and Engineers.
  • Reporting structures may vary depending on the specific product area, but Quantitative UX Researchers often report within the broader UX or Research organizations.

Methodology:

  • Google's product development often follows agile or lean methodologies, with a strong emphasis on iteration and user feedback.
  • Quantitative UX Research plays a critical role in informing these cycles through data-driven insights on user behavior, usability, and product performance.
  • The company utilizes a variety of research methods, both qualitative and quantitative, to gain a comprehensive understanding of user needs and experiences.

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

📝 Enhancement Note: Company information is publicly available. The interpretation of how company size, industry, and history impact the design/research culture and team structure is based on general knowledge of large tech companies and the specific details provided about the UX team composition and collaboration patterns.

📈 Career & Growth Analysis

Design Career Level: This role appears to be at a Mid-Senior level within Google's UX Research career ladder. It requires independent execution of research projects, collaboration with senior stakeholders (including Director level), and the ability to influence product strategy. This level typically involves leading specific research initiatives and potentially mentoring more junior researchers.

Reporting Structure: Quantitative UX Researchers at Google typically report to a UX Research Manager or Lead within a specific product area or a central research organization. They work closely with other researchers and cross-functional partners.

Design Impact: Quantitative UX Researchers at Google have significant impact by providing data-driven evidence that informs product design, feature development, and strategic decisions. Their insights are crucial for understanding the scale of user issues, validating hypotheses, and measuring the success of launched features.

Growth Opportunities:

  • Advancement to a Lead Quantitative UX Researcher role, potentially managing projects or teams.
  • Specialization in specific quantitative methodologies or product areas.
  • Opportunities to contribute to internal research tools and methodologies used across Google.
  • Mentorship opportunities for junior researchers.
  • Potential for growth into management roles within the UX Research organization.
📝 Enhancement Note: The career analysis is based on the specified experience level, responsibilities (including collaboration with executive leadership), and typical career progression paths within large tech companies for research roles. The potential for leadership and specialization is inferred from the seniority implied by the requirements.

🌐 Work Environment

Studio Type: The role is listed as on-site in São Paulo, Brazil. Google offices are known for their modern, well-equipped workspaces designed to foster collaboration and creativity. The São Paulo office likely provides a professional studio environment.

Office Location(s): The primary location is São Paulo, SP, Brazil. Google has multiple offices globally, but this role is specifically tied to the São Paulo location.

Design Workspace Context:

  • Access to dedicated workspaces, meeting rooms, and collaborative areas designed to support research activities and cross-functional teamwork.
  • Availability of necessary software, hardware, and research tools required for data analysis, programming, and reporting.
  • Opportunities for in-person interaction and collaboration with the local UX team and other product stakeholders.

Work Schedule: This is a full-time on-site role. While there might be some flexibility in daily scheduling, the expectation is regular presence in the São Paulo office to facilitate close collaboration with the team and stakeholders. The specific schedule would be determined by the team and project needs.

📝 Enhancement Note: Assumptions about the work environment are based on general knowledge of Google's office standards and the nature of on-site work in a large tech company. The specifics of the São Paulo office environment are inferred but align with common practices for fostering a productive research workspace.

📄 Application & Portfolio Review Process

Design Interview Process:

  • Initial screening call with a Recruiter to discuss your background and interest in the role.
  • Technical screen focused on your quantitative research skills, statistical knowledge, and programming abilities. Be prepared to discuss your approach to data analysis and problem-solving.
  • Portfolio review and case study presentation(s) where you walk through your most impactful quantitative research projects, highlighting your process, methodologies, findings, and impact.
  • Multiple interviews with members of the UX Research team, Product Managers, Engineers, and potentially Research Leads or Managers. These interviews will assess your technical skills, collaboration abilities, communication style, and cultural fit.
  • A potential research challenge or whiteboarding session where you may be asked to design a quantitative study to address a specific product question.

Portfolio Review Tips:

  • Focus on case studies that demonstrate your proficiency in quantitative research methods, data analysis, and programming (Python/R).
  • Clearly articulate the research question, methodology, key findings, and the impact of your work on the product or business.
  • Highlight your ability to translate complex quantitative data into clear and compelling narratives.
  • Showcase your understanding of statistical concepts and how you applied them correctly.
  • If possible, include examples of how you have worked with logs data or designed and analyzed large-scale surveys.

Challenge Preparation:

  • Review fundamental statistical concepts, experimental design principles, and common pitfalls in quantitative research.
  • Practice designing quantitative studies to address hypothetical product questions, considering appropriate metrics, data sources, and analysis methods.
  • Be prepared to explain your thought process and rationale behind your design choices.
  • Brush up on your programming skills in Python or R for data manipulation and statistical analysis.

ATS Keywords: Quantitative Research, UX Research, User Experience, Data Analysis, Statistical Analysis, Python, R, MATLAB, C++, Java, Go, Survey Design, Metrics Development, Logs Analysis, Path Modeling, Regression Analysis, Experimental Design, A/B Testing, Hypothesis Testing, Data Manipulation, Data Visualization, User Behavior, Product Analytics, HCI, Human-Computer Interaction, Statistics, Econometrics, Data Science, Behavioral Research, Cross-Functional Collaboration, Communication, Stakeholder Management, Problem Solving, Critical Thinking, Applied Research, Product Development, User Insights, Empirical Research, Quantitative Methods, Data Interpretation, Research Design.

📝 Enhancement Note: The interview process and preparation advice are based on typical hiring practices for quantitative research roles at major tech companies like Google. The emphasis on technical skills, case studies demonstrating quantitative rigor, and cross-functional collaboration aligns with the requirements outlined in the job description.

🛠 Tools & Technology Stack

Primary Design Tools (Quantitative Research Context):

  • Python: Essential for data manipulation, statistical analysis, and developing custom research scripts. Expect to use libraries like Pandas, NumPy, SciPy, StatsModels, and scikit-learn.
  • R: Another primary tool for statistical computing, data analysis, and visualization. Proficiency with packages like tidyverse, ggplot2, and statistical testing libraries is valuable.
  • SQL: Likely required for querying and extracting data from internal databases, especially for logs analysis.
  • Statistical Software: While Python and R are primary, familiarity with other statistical software (e.g., SAS, SPSS) could be beneficial.

Collaboration & Handoff:

  • Internal Google Tools: Proficiency with Google's internal collaboration platforms and data infrastructure will be necessary.
  • Presentation Software: Tools like Google Slides or similar for creating compelling presentations of research findings.
  • Documentation Platforms: Potentially internal tools or standard platforms for documenting research methodologies, findings, and recommendations.

Research & Testing:

  • Survey Platforms: Experience with designing and deploying surveys using various platforms.
  • A/B Testing Frameworks: Understanding of A/B testing principles and potentially experience with related internal tools for experimental design and analysis.
  • Analytics Platforms: Familiarity with product analytics platforms for understanding user behavior at scale.
📝 Enhancement Note: The tools and technology stack are inferred based on the requirements for programming languages (Python, R), statistical analysis, and the nature of quantitative research involving logs analysis and surveys. SQL is a common requirement for accessing large datasets in tech companies. Google's internal tools are acknowledged as a likely part of the environment.

👥 Team Culture & Values

Design Values (within the Quantitative Research context):

  • User Focus: A deep commitment to understanding user needs and behaviors through rigorous data analysis to inform product decisions.
  • Data-Driven Decision Making: Valuing empirical evidence and statistical insights as the foundation for informing product strategy and design.
  • Innovation and Exploration: Encouraging the exploration of new quantitative methods and approaches to uncover deeper user insights.
  • Collaboration and Knowledge Sharing: Fostering a collaborative environment where researchers share findings, methodologies, and learn from each other.

Collaboration Style:

  • Highly collaborative with cross-functional partners (Designers, Product Managers, Engineers, Data Scientists) to ensure research is relevant and impactful.
  • Emphasis on clear and concise communication of complex quantitative findings to diverse audiences.
  • Participation in design critiques and product reviews, providing data-driven perspectives.
  • Engagement with the broader Quantitative UXR community at Google through meetups and knowledge-sharing sessions.
📝 Enhancement Note: The description of team culture and values is based on Google's stated premise ("Focus on the user and all else will follow"), the emphasis on collaboration with a multi-disciplinary team, and the nature of quantitative research which inherently values data and empirical evidence.

⚡ Challenges & Growth Opportunities

Design Challenges (Quantitative Research Context):

  • Navigating the complexity of large-scale datasets and ensuring data quality and validity for analysis.
  • Effectively translating complex statistical findings into actionable insights that resonate with product teams and stakeholders.
  • Prioritizing research opportunities across multiple product initiatives to maximize impact within limited resources.
  • Staying current with the latest quantitative research methodologies and tools in a rapidly evolving field.

Learning & Development Opportunities:

  • Access to internal training programs and resources for developing advanced statistical and programming skills.
  • Opportunities to attend industry conferences and workshops related to UX research, statistics, and data science.
  • Mentorship programs within the Google UXR community.
  • Opportunities to work on diverse product areas and research questions, expanding your expertise.
  • Potential for contributing to the development of internal research tools and best practices.
📝 Enhancement Note: The identification of challenges is based on common difficulties faced by quantitative researchers working with large datasets in complex product environments. Growth opportunities are inferred from Google's size, culture of learning, and the presence of an internal UXR community.

💡 Interview Preparation

Design Process Questions (Quantitative Research Focus):

  • Describe your process for scoping and designing a quantitative UX research study from beginning to end. Be prepared to discuss how you formulate research questions, select appropriate methodologies (e.g., survey vs. logs analysis), and determine necessary sample sizes or data requirements.
  • Walk me through a time you used statistical analysis to uncover a significant user insight. Explain the statistical methods you applied, the results, and how you ensured the validity of your findings. Use a case study from your portfolio.
  • How do you translate complex statistical results into clear, actionable recommendations for non-technical stakeholders (e.g., Designers, Product Managers)? Provide examples of how you have effectively communicated quantitative findings.

Company Culture Questions (Quantitative Research Context):

  • How do you approach collaboration with cross-functional teams (Design, PM, Eng, Data Science) to ensure your research is integrated into the product development process?
  • Describe your experience working with large datasets, such as product logs. What are some of the challenges you've encountered, and how have you addressed them?
  • How do you prioritize research requests and manage your workload when supporting multiple product initiatives?

Portfolio Presentation Strategy (Quantitative Focus):

  • Structure your case studies to clearly demonstrate your quantitative research process: problem, hypothesis, methodology (including data sources, statistical methods, and tools used), findings (with appropriate visualizations and statistical evidence), insights, and impact.
  • For each case study, explain the specific programming code or statistical models you developed and highlight your technical proficiency.
  • Be prepared to discuss the limitations of your research and any assumptions you made during the analysis.
  • Focus on the actionable insights derived from your quantitative findings and how they influenced product decisions or outcomes.
📝 Enhancement Note: The interview questions and preparation advice are tailored to assess the specific skills and experience required for a Quantitative UX Researcher role at Google, focusing on technical proficiency, research methodology, data analysis, and cross-functional collaboration. The portfolio tips emphasize demonstrating the rigor and impact of quantitative work.

📌 Application Steps

To apply for this design position:

  • Submit your application through this link
  • Customize your resume to highlight your quantitative research experience, statistical skills, programming proficiency (Python/R), and experience with survey design and logs analysis. Use relevant ATS keywords.
  • Prepare a portfolio or case studies that specifically showcase your quantitative research projects, focusing on your process, methodologies, data analysis techniques, insights, and demonstrated impact on product or user experience.
  • Practice presenting your case studies, focusing on clearly articulating your research approach and the significance of your quantitative findings.
  • Research Google's products and recent announcements, particularly those related to user experience and data-driven decision-making, to demonstrate your understanding of the company's focus.
⚠️ Important Notice: This enhanced job description includes AI-generated insights and design industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.
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