Senior Quantitative UX Researcher, Merchant Shopping

📍 Job Overview

Job Title: Senior Quantitative UX Researcher, Merchant Shopping

Company: Google

Location: Zürich, ZH, Switzerland

Job Type: Full-time

Category: User Experience Research / Data Science

Date Posted: April 24, 2026

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

Remote Status: On-site

🚀 Role Summary

• Lead quantitative user experience research initiatives focused on understanding merchant behaviors and integrating AI into UX workflows for the Merchant Shopping division.

• Drive product strategy and user experience enhancements through robust data-driven recommendations, supported by compelling data visualizations and organizational narratives.

• Own the development and measurement of user experience metrics in collaboration with data science teams, ensuring a scalable understanding of merchant users across various platforms.

• Contribute to the growth and expertise of the Quantitative UXR team, elevating quantitative research craft and knowledge sharing among a community of 20+ UXRs.

• Utilize advanced analytical skills and programming languages (Python, R, MATLAB) to clean, join, and analyze diverse datasets, including logs and surveys, to extract actionable insights.

📝 Enhancement Note: This role is positioned as a Senior Quantitative UX Researcher, implying a significant level of autonomy and influence. The focus on "AI-integrated workflows for UXers" and "merchant users across a large spectrum of merchants from SMBs to large retailers" suggests a strategic role that bridges cutting-edge technology with core business objectives in the e-commerce and retail analytics space. The mention of influencing "product strategy" and owning "program ownership" indicates a need for strong leadership and strategic thinking beyond just executing research.

📈 Primary Responsibilities

• Spearhead quantitative research programs to deeply understand the behaviors, needs, and pain points of merchants (SMBs to large retailers) interacting with Google's shopping platforms.

• Design and implement novel AI-integrated workflows and research methodologies to enhance the efficiency and effectiveness of the UX research team.

• Translate complex quantitative findings into clear, actionable insights and compelling data visualizations and narratives for diverse stakeholders, including Product Management, Engineering, and Executive leadership.

• Develop and manage key user experience metrics and measurement frameworks in close partnership with Data Science to track product performance and user satisfaction at scale.

• Own the research roadmap and execution for specific merchant segments or product surfaces, driving research initiatives from conception through to impact measurement.

• Conduct advanced statistical analysis, including segmentation, regression, and predictive modeling, to identify user trends and opportunities for product innovation.

• Collaborate with qualitative UX researchers to triangulate findings and provide a holistic understanding of the user experience.

• Contribute to the intellectual capital of the Quantitative UXR team by sharing best practices, mentoring junior researchers, and participating in cross-functional learning initiatives.

• Stay abreast of the latest advancements in AI, Machine Learning, Natural Language Processing, and their applications within UX research and product development.

📝 Enhancement Note: The responsibilities emphasize a blend of deep analytical expertise, strategic product influence, and team leadership within the quantitative UX research domain. The specific mention of "AI-integrated workflows for UXers" and "merchant users across a large spectrum of merchants" points to a specialized focus within Google's Merchant Shopping division, requiring a researcher who can not only analyze data but also shape how research itself is conducted and leveraged.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, Computer Science, or a related quantitative field, or equivalent practical experience.

Experience:

• Minimum of 6 years of experience in an applied research setting, with a strong focus on quantitative methodologies and data analysis.

• Demonstrated experience in cleaning, joining, and analyzing large, complex datasets, including user logs, survey data, and behavioral metrics.

Required Skills:

• Expertise in quantitative research methodologies, including experimental design, survey design, statistical analysis (e.g., A/B testing, regression analysis), and log analysis.

• Proficiency in programming languages commonly used for data manipulation and computational statistics, such as Python (with libraries like Pandas, NumPy, SciPy) or R.

• Experience with data visualization tools and techniques to effectively communicate complex findings to technical and non-technical audiences (e.g., Tableau, Looker, Matplotlib, Seaborn).

• Solid understanding of Machine Learning (ML) models, ML infrastructure, Natural Language Processing (NLP), or Deep Learning concepts and their application in user research or product development.

• Experience in conducting research with a focus on user behavior, product strategy, and user experience measurement.

Preferred Skills:

• Experience leveraging a diverse range of qualitative and quantitative research methods and analysis techniques across various disciplinary environments.

• Experience in developing and implementing AI/ML models for research purposes or analyzing AI-driven product features.

• Ability to participate effectively in technical discussions related to AI, ML, and data infrastructure.

• Passion for AI technology and its potential to transform user experiences and business outcomes.

• Experience conducting research within the e-commerce, retail, or merchant services domain.

• Familiarity with Google's internal research tools and platforms.

📝 Enhancement Note: The emphasis on quantitative skills, programming languages, and ML/AI knowledge is critical. The preferred qualifications highlight a desire for candidates who can bridge technical depth with a broad understanding of research methodologies and a forward-thinking approach to AI's role in UX.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase at least 2-3 detailed case studies demonstrating significant quantitative UX research projects, ideally involving large datasets and complex analysis.

• For each case study, clearly articulate the research problem, your specific role and contribution, the methodologies and tools used (highlighting Python/R, ML/AI applications), the key findings, and the measurable impact on product strategy or user experience.

• Include examples of sophisticated data visualizations and narratives that effectively communicate complex insights to diverse audiences.

• Demonstrate experience in defining and tracking key performance indicators (KPIs) and user experience metrics, and how these were used to drive product decisions.

Process Documentation:

• Examples of how you have documented research processes, including experimental designs, survey instruments, data analysis plans, and reporting templates.

• Evidence of developing and optimizing research processes for efficiency and scalability, particularly in a fast-paced product development environment.

• Documentation illustrating collaboration with Data Science, Product Management, and Engineering teams on research initiatives and metric definition.

📝 Enhancement Note: For a quantitative UX researcher role at Google, the portfolio is paramount. It needs to provide concrete evidence of analytical rigor, strategic impact, and technical proficiency. The emphasis should be on showcasing complex problem-solving, data-driven decision-making, and the ability to translate technical findings into business value, particularly concerning AI and merchant user behavior.

💵 Compensation & Benefits

Salary Range:

Given the Senior Quantitative UX Researcher title, location in Zurich, and the employer being Google, the estimated salary range is CHF 130,000 - CHF 180,000 per year. This range is based on industry benchmarks for senior-level research roles in major tech companies in Switzerland, considering the cost of living and demand for specialized quantitative and AI/ML expertise.

Benefits:

• Comprehensive health, dental, and vision insurance plans.

• Generous paid time off, including vacation, sick leave, and public holidays.

• Retirement savings plan with employer matching contributions.

• Stock options or Restricted Stock Units (RSUs) as part of compensation.

• Professional development opportunities, including access to training, conferences, and internal learning resources.

• Mentorship programs and a supportive UXR community for knowledge sharing and career growth.

• Access to exclusive internal tools and technologies developed by Google.

• Parental leave and family support benefits.

• On-site amenities (depending on office location) such as cafeterias, fitness centers, and transportation support.

Working Hours:

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

• Flexibility in working hours may be available, with core hours expected for team collaboration and meetings.

• The role is on-site, requiring physical presence at the Zürich office.

📝 Enhancement Note: Salary estimates are based on Google's typical compensation structures for senior roles in high-cost-of-living European tech hubs and the specialized nature of quantitative UX research with AI/ML focus. Benefits are standard for large tech companies, with a particular emphasis on professional development and community as highlighted in the job description.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Internet Services and Software, AI/ML, E-commerce)

Company Size: 10,000+ employees (Global)

Founded: 1998 (Google)

Team Structure:

• The role is within the "Merchant Shopping" division, a critical area for Google's e-commerce and advertising strategy.

• You will be part of a multi-disciplinary UX team, collaborating closely with Product Managers, Software Engineers, Data Scientists, and other UX Researchers (both qualitative and quantitative).

• The Quantitative UXR team is a specialized group within the broader UX organization, likely numbering 20+ researchers, fostering a strong community of practice.

Methodology:

• Data-driven decision-making is a core tenet at Google, with a strong emphasis on rigorous quantitative analysis and user behavior modeling.

• The team utilizes a wide array of research methods, from large-scale surveys and log analysis to advanced statistical modeling and ML applications.

• There's a significant focus on understanding user needs at scale and translating these insights into actionable product strategies.

• The integration of AI and ML into research processes and product features is a key strategic priority for this role.

• Collaboration and knowledge sharing are actively encouraged through internal communities and development programs.

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

📝 Enhancement Note: Google's culture is renowned for its data-centricity, innovation, and focus on user needs. For this role, the "Merchant Shopping" context implies a direct impact on how businesses use Google's platforms to reach customers and how consumers discover and purchase products. The emphasis on AI and quantitative rigor aligns with Google's broader strategic investments.

📈 Career & Growth Analysis

Operations Career Level:

This "Senior Quantitative UX Researcher" position is typically a mid-to-senior level role. It signifies a researcher who can independently lead complex research projects, drive strategic product decisions, and mentor others. This level requires a deep understanding of quantitative methodologies, advanced analytical skills, and the ability to influence cross-functional teams and product roadmaps.

Reporting Structure:

You will likely report to a UX Research Lead or Manager within the Merchant Shopping product group. This lead will guide your strategic direction, provide mentorship, and support your career development. You will collaborate extensively with Product Managers, Engineers, and Data Scientists who are peers in influence and impact, though not in direct reporting lines.

Operations Impact:

The impact of this role is substantial. By understanding merchant user behavior at scale and leveraging AI, you will directly influence the design and effectiveness of Google's shopping products. This includes improving how merchants utilize Google's advertising and shopping tools, thereby enhancing their business outcomes and contributing to Google's revenue growth. Your data-driven recommendations will shape product strategy, optimize user journeys, and ensure that Google's offerings meet the evolving needs of merchants in the digital economy.

Growth Opportunities:

• Specialization: Deepen expertise in AI/ML applications for UX research, advanced statistical modeling, or specific merchant segments within e-commerce.

• Leadership: Transition into a Lead UX Researcher or Managerial role, overseeing research teams and strategic initiatives.

• Cross-functional Mobility: Move into Product Management, Data Science, or specialized AI/ML research roles within Google.

• Technical Advancement: Become a go-to expert for quantitative methods and AI integration within the broader UX organization.

• Mentorship: Develop leadership skills by mentoring junior researchers and contributing to the growth of the UXR community.

📝 Enhancement Note: The "Senior" title at Google implies significant responsibility and potential for future growth. The role offers a clear path for deepening expertise in a high-demand area (AI/ML in UX) and potentially moving into leadership or more specialized technical tracks within the company.

🌐 Work Environment

Office Type:

This role is on-site in Google's Zürich office. Google offices are typically designed to foster collaboration, innovation, and employee well-being. Expect a modern, well-equipped workspace with a focus on functionality and comfort.

Office Location(s):

The primary location for this role is Google's office in Zürich, Switzerland. This location offers a vibrant urban environment with excellent infrastructure and accessibility.

Workspace Context:

• Collaborative Environment: Open-plan desk areas are common, interspersed with numerous meeting rooms, quiet focus zones, and collaborative brainstorming spaces. The design encourages spontaneous interactions and teamwork.

• Technology & Tools: Access to high-performance computing resources, cutting-edge software, and internal Google research platforms will be standard. High-speed internet and robust IT support are a given.

• Team Interaction: Frequent face-to-face interactions with product teams, fellow researchers, and cross-functional partners are expected. This environment supports agile development cycles and rapid iteration based on research insights.

• Amenities: Google offices often feature amenities like on-site cafeterias with diverse food options, fitness centers, relaxation areas, and sometimes even game rooms, all designed to support employee productivity and well-being.

Work Schedule:

• The role is on-site in Zürich, requiring a consistent presence in the office.

• While a standard 40-hour work week is typical, Google often offers flexibility in daily start and end times, provided core collaboration hours are met and project deadlines are achieved.

• The fast-paced nature of product development means occasional periods of intense work might be necessary to meet critical launch dates or research milestones.

📝 Enhancement Note: The on-site requirement in Zürich means candidates should be prepared for and comfortable with working from a physical Google office. The description emphasizes a highly collaborative and technologically advanced environment designed to support innovation and productivity.

📄 Application & Portfolio Review Process

Interview Process:

The interview process at Google is typically rigorous and multi-staged, designed to assess technical skills, problem-solving abilities, leadership potential, and cultural fit. For a Senior Quantitative UX Researcher role, expect the following:

1. Recruiter Screen: An initial conversation to assess basic qualifications, experience, and interest in the role.

2. Hiring Manager Interview: Focus on your experience, research philosophy, leadership style, and how you approach complex problems. This is also an opportunity to learn more about the team and role.

3. Technical/Research Interviews (2-4 rounds):

• Quantitative Skills Assessment: Deep dive into your statistical knowledge, experience with programming languages (Python/R), data analysis techniques, and ML/AI concepts. You may be asked to walk through analytical approaches to hypothetical problems.
• Research Design & Methodology: Questions about how you design studies, choose appropriate methods, handle data challenges, and ensure research rigor.
• Product Sense & Strategy: Discussions on how you use research to influence product strategy, identify user needs, and measure impact. You might be asked to critique existing products or propose research for new features.
• Portfolio Review: A dedicated session where you will present 1-2 key projects from your portfolio, detailing your process, findings, and impact.

1. Cross-functional Interview: An interview with a peer from Product Management, Engineering, or Data Science to assess collaboration skills and ability to work effectively in a team.

2. "Googliness" Interview: Assesses cultural fit, leadership potential, comfort with ambiguity, collaborative spirit, and proactive problem-solving.

Portfolio Review Tips:

• Structure: For each case study, use a clear narrative structure: Problem/Opportunity -> Your Role & Approach -> Methodology & Data -> Key Findings -> Impact & Recommendations -> Learnings.

• Quantify Impact: Whenever possible, use metrics to demonstrate the business or user impact of your research. For example, "Our recommendations led to a X% increase in conversion rates" or " Identified a critical usability issue that affected Y% of users, leading to a redesign that reduced task completion time by Z%."

• Highlight Technical Skills: Be ready to discuss your code (Python/R) and analytical process in detail. Explain why you chose specific statistical methods or ML models.

• Showcase AI/ML Relevance: If you have projects involving AI/ML (either analyzing AI features or using ML for research), highlight them prominently. Explain the problem, your approach, and the insights derived.

• Tailor to the Role: Emphasize projects that align with understanding merchant behavior, influencing product strategy at scale, and working with large datasets.

• Visualizations: Ensure your data visualizations are clear, informative, and visually compelling. Be prepared to explain your design choices.

• Conciseness: Be prepared to present your key projects within a given timeframe (e.g., 30-45 minutes including Q&A).

Challenge Preparation:

• Data Analysis/Coding Challenge: You might be given a dataset and asked to perform specific analyses or write code snippets to answer research questions. Practice common data manipulation and statistical tasks in Python/R.

• Research Design Scenario: You may be presented with a product scenario and asked to design a quantitative research study to address a specific question. Focus on defining clear objectives, choosing appropriate methods, identifying key metrics, and outlining potential challenges.

• Problem-Solving: Be ready for hypothetical questions that assess your ability to break down complex problems, think critically, and propose data-driven solutions. Practice articulating your thought process clearly.

📝 Enhancement Note: Google's interview process is known for its depth. Candidates should prepare to demonstrate not just what they've done, but how they've done it, and why. The portfolio review is a critical component for this role, requiring candidates to present tangible evidence of their quantitative expertise and strategic impact.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (with libraries like Pandas, NumPy, SciPy, Scikit-learn, TensorFlow/PyTorch for ML), R (with tidyverse, caret, etc.), potentially MATLAB.

• Data Analysis & Statistical Software: Proficiency in statistical modeling, hypothesis testing, regression analysis, experimental design, and potentially Bayesian methods.

• Data Visualization Tools: Tableau, Looker, Matplotlib, Seaborn, ggplot2, D3.js (for web-based visualizations).

Analytics & Reporting:

• Big Data Platforms: Experience with large-scale data processing frameworks like Apache Spark, Hadoop, or Google's internal equivalents (e.g., Colossus, Borg).

• SQL: Essential for querying and manipulating data from relational databases.

• A/B Testing Platforms: Experience designing, implementing, and analyzing A/B tests.

• Web Analytics Tools: Google Analytics or similar platforms for understanding user behavior on web properties.

CRM & Automation:

• While not the primary focus, familiarity with how CRM data (e.g., Salesforce) or customer data platforms (CDPs) integrate with research datasets can be beneficial for understanding the merchant lifecycle.

• Experience with survey platforms (e.g., Qualtrics, SurveyMonkey) for data collection.

📝 Enhancement Note: The technical stack is heavily weighted towards data analysis, programming, and ML/AI. Proficiency in Python and R is non-negotiable, and experience with large-scale data processing and advanced statistical techniques is expected.

👥 Team Culture & Values

Operations Values:

• User Focus: "Focus on the user and all else will follow" is Google's guiding principle. This means deeply understanding merchant needs and ensuring products serve them effectively.

• Data-Driven Decision Making: Relying on rigorous quantitative analysis to inform product strategy, design choices, and business outcomes.

• Innovation & Experimentation: A culture that encourages exploring new methodologies, technologies (like AI/ML), and approaches to research and product development.

• Collaboration & Teamwork: Working effectively across disciplines (UX, Eng, PM, DS) to achieve shared goals. Open communication and constructive feedback are valued.

• Impact & Ownership: Taking initiative, owning projects, and driving measurable impact on users and the business.

• Continuous Learning: Staying curious, adapting to new technologies and methodologies, and actively seeking opportunities for professional growth.

Collaboration Style:

• Cross-functional Integration: Researchers are embedded within product teams, working closely with Product Managers, Engineers, and Data Scientists daily.

• Data-Driven Dialogue: Discussions are often grounded in data and empirical evidence, fostering objective problem-solving.

• Iterative Process: Collaboration supports an agile, iterative approach to product development, where research insights are continuously fed back into the design and engineering cycles.

• Knowledge Sharing: A strong emphasis on sharing learnings, best practices, and insights across teams and the broader UXR community through presentations, internal documentation, and community forums.

📝 Enhancement Note: The values at Google, particularly for this role, emphasize a blend of deep analytical rigor, user empathy, technical innovation (especially with AI), and a collaborative, impact-oriented work ethic.

⚡ Challenges & Growth Opportunities

Challenges:

• Scale and Complexity: Understanding the diverse needs of a wide spectrum of merchants (SMBs to large enterprises) across various product surfaces presents a significant analytical challenge.

• AI Integration Complexity: Developing and implementing AI-integrated workflows for UXers requires navigating technical complexities and ensuring practical applicability.

• Data Silos & Integration: Working with large, potentially siloed datasets and ensuring seamless data integration for comprehensive analysis.

• Driving Product Strategy: Translating complex quantitative findings into actionable product strategies that gain organizational buy-in and lead to tangible improvements.

• Rapidly Evolving Landscape: Keeping pace with advancements in AI, ML, and e-commerce trends to ensure research remains relevant and impactful.

Learning & Development Opportunities:

• Advanced AI/ML Training: Opportunities to deepen knowledge and practical application of AI/ML models relevant to UX research and product development.

• Quantitative Methodology Workshops: Access to internal Google training on cutting-edge statistical techniques, experimental design, and data analysis.

• Cross-Disciplinary Learning: Exposure to the work of Data Scientists, Engineers, and Product Managers, broadening your understanding of the product development lifecycle.

• Industry Conferences & Publications: Potential for support to attend leading UX, AI, and data science conferences and opportunities to contribute to internal or external publications.

• Mentorship: Benefit from mentorship from senior researchers and leaders within Google, guiding career progression and skill development.

📝 Enhancement Note: This role offers the challenge of working at the forefront of AI and quantitative research within a massive tech organization, providing ample opportunities for professional growth and impact.

💡 Interview Preparation

Strategy Questions:

• Quantitative Research Design: "Imagine we want to understand why SMB merchants are not adopting Feature X. How would you design a quantitative study to investigate this, considering we have access to user logs, survey data, and CRM information? What metrics would you focus on?"

  • Preparation: Practice outlining research objectives, methodology (survey, A/B test, log analysis), key metrics, potential biases, and how you'd analyze the data.

• AI/ML Application: "How could AI/ML be used to improve the efficiency or effectiveness of quantitative UX research for merchant shopping products? Provide specific examples."

  • Preparation: Think about areas like automated data labeling, predictive modeling of user behavior, anomaly detection in user journeys, or personalized research insights.

• Data Analysis & Interpretation: "You've run a regression analysis and found a significant correlation between Merchant Engagement Score and Conversion Rate. How would you interpret this finding, what follow-up analyses would you conduct, and how would you present this to a Product Manager?"

  • Preparation: Focus on correlation vs. causation, potential confounding variables, statistical significance, and clear communication strategies.

• Product Strategy Influence: "Describe a time when your quantitative research significantly influenced product strategy or design. What was the problem, your approach, the key insight, and the ultimate impact?"

Company & Culture Questions:

• "What interests you specifically about Google's Merchant Shopping division and this quantitative UXR role?"

  • Preparation: Research the Merchant Shopping products, Google's strategy in e-commerce, and connect it to your skills and interests.

• "How do you approach collaborating with data scientists who may have different priorities or perspectives?"

  • Preparation: Emphasize shared goals, clear communication, mutual respect, and finding common ground through data.

• "How do you stay updated on the latest trends in AI, ML, and quantitative research?"

Portfolio Presentation Strategy:

• Focus on Impact: For each case study, clearly articulate the problem, your analytical approach, the key insights derived, and the measurable business or user impact. Use data to tell the story.

• Demonstrate Technical Depth: Be prepared to discuss your code, statistical models, and analytical choices in detail. Explain why you chose certain methods.

• Showcase Communication Skills: Ensure your slides are clean, visually appealing, and effectively communicate complex information. Practice presenting your findings clearly and concisely.

• Highlight AI/ML Relevance: If applicable, explicitly call out any projects involving AI/ML, explaining your role and the outcomes.

• Anticipate Questions: Think about potential questions related to methodology, data limitations, alternative approaches, and stakeholder management.

📝 Enhancement Note: Google interviews are designed to be challenging and comprehensive. Candidates should be prepared to demonstrate deep technical expertise, strategic thinking, and strong communication skills, backed by concrete examples from their portfolio.

📌 Application Steps

To apply for this Senior Quantitative UX Researcher position:

• Submit your application through the Google Careers portal via the provided link.

• Portfolio Customization: Tailor your portfolio to highlight 2-3 of your most impactful quantitative UX research projects. Prioritize those that demonstrate experience with large datasets, advanced statistical analysis, AI/ML applications, influencing product strategy, and working with merchant or e-commerce contexts. Ensure each case study clearly outlines the problem, your role, methodology, findings, and measurable impact.

• Resume Optimization: Update your resume to prominently feature keywords related to quantitative UX research, data analysis, Python, R, Machine Learning, AI, statistical modeling, and product strategy. Quantify your achievements wherever possible (e.g., "Increased conversion rates by X% through insights from Y study").

• Interview Preparation: Thoroughly review common quantitative UX research interview questions, focusing on study design, data analysis scenarios, and your experience influencing product decisions. Practice articulating your thought process and preparing your portfolio presentation. Research Google's products and the Merchant Shopping division.

• Company Research: Understand Google's mission, values, and its strategic focus on AI and e-commerce. Familiarize yourself with the "Focus on the user" principle and how it applies to merchant solutions.

⚠️ 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.