Quantitative UX Researcher, Customer Engagement

📍 Job Overview

Job Title: Quantitative UX Researcher, Customer Engagement

Company: Google

Location: Mountain View, California, United States

Job Type: Full-Time

Category: User Experience Research / Product Analytics

Date Posted: August 31, 2026

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

Remote Status: On-site

🚀 Role Summary

• Drive product innovation and user-centric design through rigorous quantitative UX research methodologies.

• Analyze complex user behavior within Google's Customer Relationship Management (CRM) platform, focusing on agentic AI and multi-agent systems.

• Design and execute empirical studies, including log analysis, surveys, and A/B experiments, to uncover actionable insights.

• Collaborate with cross-functional teams (Design, Product Management, Engineering, Data Science) to define success metrics and inform product strategy.

• Translate sophisticated statistical findings into clear, impactful recommendations for executive leadership.

📝 Enhancement Note: This role is specifically focused on the quantitative analysis of user interactions within Google's internal CRM platform that supports Google Ads Sales. The "Customer Engagement" aspect likely refers to how sellers and advertisers engage with this platform and its AI-driven features. The emphasis on "agentic AI" and "multi-agent systems" indicates a cutting-edge research area within Google, requiring a strong analytical and statistical background.

📈 Primary Responsibilities

• Develop and implement quantitative UX goals and key performance indicators (KPIs) in close partnership with UX Designers, Qualitative Researchers, Data Scientists, and Program Managers.

• Design, conduct, and analyze human and synthetic evaluation systems for complex multi-agent workflows, employing advanced statistical models (e.g., crossed rater/item models) to assess routing quality and identify/mitigate rater bias.

• Architect, execute, and interpret results from A/B experiments to rigorously measure the impact of product enhancements and new features on user behavior and critical business metrics.

• Collaborate proactively with cross-functional partners to define user-centered success metrics and ensure robust data instrumentation for accurate measurement and analysis.

• Synthesize complex research findings into clear, concise, and actionable insights, effectively communicating these to diverse stakeholders, including executive leadership, to drive data-informed product improvements and enhance the overall user experience.

• Investigate user behavior through empirical methods such as log analysis, survey research, and regression modeling to uncover actionable insights.

• Define measurement frameworks and behavioral models for understanding user interaction with multi-agent systems, focusing on platform-level questions related to routing efficiency, AI quality calibration, and human-in-the-loop patterns.

📝 Enhancement Note: The responsibilities highlight a deep dive into advanced statistical analysis and experimental design, particularly within the context of AI-driven systems. The focus on "evaluating multi-agent architectures, agentic workflows, orchestrators, or subagent routing systems" suggests a specialized research area requiring expertise beyond standard UX research. The expectation to work with executive leadership implies a need for strong communication and strategic thinking skills.

🎓 Skills & Qualifications

Education:

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

• Master's degree or PhD in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, or a related quantitative field is strongly preferred, indicating a desire for advanced analytical training. Experience:

• Minimum of 6 years of experience in product research within an applied research setting, or a comparable role.

• Preferred experience includes 5 years of conducting UX research on products and engaging with executive leadership (Director level and above).

• Preferred experience of 3 years in project management, particularly within large, matrixed organizations.

• Experience evaluating user behavior in Business-to-Business (B2B) contexts, enterprise software, Customer Relationship Management (CRM) platforms, or support tooling is highly desirable.

• Experience evaluating multi-agent architectures, agentic workflows, orchestrators, or subagent routing systems is a significant plus. Required Skills:

• Proficiency in programming languages for data manipulation and computational statistics, such as Python, R, MATLAB, C++, Java, or Go.

• Demonstrated experience applying statistical modeling techniques, including mixed-effects modeling and item response modeling, to behavioral data.

• Expertise in experimental design and survey research instrument design for evaluating complex systems.

• Strong foundation in quantitative UX research methodologies and data analysis.

• Ability to translate complex findings into actionable insights and communicate them effectively to diverse stakeholders. Preferred Skills:

• Deep understanding of Human-Computer Interaction (HCI) principles and user-centered design.

• Experience with advanced statistical techniques relevant to AI and machine learning evaluation.

• Familiarity with CRM platforms, enterprise software, and B2B user behavior.

• Experience in evaluating agentic AI systems and multi-agent architectures.

• Proven ability to manage projects and collaborate effectively in a large, matrixed organizational structure.

📝 Enhancement Note: The qualifications emphasize a strong blend of technical programming skills, advanced statistical expertise, and applied research experience. The distinction between minimum and preferred qualifications suggests that candidates with Master's/PhD degrees and direct experience with AI/multi-agent systems will be highly competitive. The mention of B2B, enterprise software, and CRM platforms points towards the specific domain where this research will be applied.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of rigorous quantitative research projects, showcasing the full lifecycle from problem definition to actionable insights.

• Case studies detailing the design and execution of complex experiments (e.g., A/B tests, survey designs) with clear articulation of hypotheses and methodologies.

• Examples of statistical modeling applied to behavioral data, highlighting the chosen models and the insights derived.

• Evidence of translating complex data findings into clear, compelling narratives and recommendations for product or business stakeholders.

• Documentation of experience with data instrumentation, metric definition, and ongoing performance monitoring. Process Documentation:

• Showcase the process for defining quantitative UX metrics and goals in collaboration with cross-functional teams.

• Detail the methodology used for designing and analyzing evaluation systems for AI-driven workflows, including steps for calibrating quality and mitigating bias.

• Illustrate the process for designing, implementing, and analyzing A/B experiments, from hypothesis generation to impact measurement.

• Provide examples of how user behavior is investigated through methods like log analysis, survey research, and regression.

📝 Enhancement Note: For a Quantitative UX Researcher role, especially at Google, a portfolio is crucial. It should not just list projects but demonstrate a deep understanding of the research process, statistical rigor, and the ability to drive impact. The emphasis on "agentic AI" and "multi-agent systems" means candidates should highlight any relevant experience in these cutting-edge areas, even if it's from academic research.

💵 Compensation & Benefits

Salary Range: $159,000 - $230,000 (USD) annually.

• This range is for the US market and is determined by factors including job-related skills, experience, and relevant education or training. Benefits:

• 15% bonus target: Performance-based incentive compensation.

• Equity: Stock options or grants, providing ownership and long-term incentive.

• Health Insurance: Comprehensive medical, dental, and vision coverage.

• Additional benefits often include: Retirement savings plans (e.g., 401k with company match), paid time off, parental leave, life insurance, disability insurance, wellness programs, and employee assistance programs. Working Hours:

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

• While on-site, there may be flexibility in daily start/end times, but the role requires consistent presence for collaboration and project work.

📝 Enhancement Note: The provided salary range is competitive for a mid-senior to senior Quantitative UX Researcher role in the Silicon Valley area, especially within a top-tier tech company like Google. The bonus and equity components are significant additions to the base salary, reflecting Google's compensation philosophy for influential roles. The mention of "executive leadership" in preferred qualifications suggests that candidates closer to the higher end of this range would likely possess extensive experience and a proven track record of strategic impact.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Internet Services & Software)

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

Founded: 1998

Company Description: Google, a subsidiary of Alphabet Inc., is a global technology leader focused on making information universally accessible and useful. Its core business revolves around search, advertising, cloud computing, software, and hardware. Google is known for its data-driven approach, innovation, and a culture that encourages experimentation and intellectual curiosity.

Team Structure:

• The Quantitative UX Researcher will be part of a multi-disciplinary team, working closely with Product Management, Engineering, Data Science, and Qualitative UX Researchers.

• This specific role is within the Google Ads organization, focusing on the Customer Relationship Management (CRM) platform that powers Google Ads Sales.

• The team is likely structured around specific product areas or platform initiatives, with this role focusing on the emerging agentic AI and multi-agent systems within the CRM. Methodology:

• Data Analysis & Insights: Heavy reliance on empirical methods, statistical modeling, and log analysis to derive actionable insights.

• Workflow Planning & Optimization: Focus on understanding and improving user workflows, particularly within AI-driven systems, to enhance efficiency and effectiveness.

• Automation & Efficiency: Research aims to improve the efficiency of seller and advertiser tasks through intelligent automation and optimized agent routing.

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

📝 Enhancement Note: Google's culture is renowned for its emphasis on data, innovation, and user focus ("Focus on the user and all else will follow"). For a Quantitative UX Researcher, this translates to an environment where rigorous data analysis and evidence-based decision-making are highly valued. The "large, matrixed organization" aspect means navigating complex stakeholder landscapes and collaborating across numerous teams will be standard. The specific focus on the Google Ads CRM platform with agentic AI suggests a cutting-edge, high-impact area within a core Google product.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned at a mid-to-senior level, requiring significant independent research capabilities and the ability to influence product strategy. It's not a junior or entry-level position, demanding substantial experience in quantitative research and statistical analysis. The expectation to work with executive leadership further elevates the role's seniority.

Reporting Structure: The Quantitative UX Researcher will likely report into a UX Research Manager or a Lead Data Scientist/Product Lead within the Google Ads organization. They will collaborate closely with peers in Design, Product Management, and Engineering, and will need to navigate reporting lines within a large, matrixed structure.

Operations Impact: This role has a direct impact on the effectiveness and efficiency of the Google Ads Sales team and, by extension, advertiser success. By improving the CRM platform's usability and AI capabilities, the researcher contributes to better sales outcomes, increased productivity, and enhanced user satisfaction with Google's advertising tools. The insights generated will directly inform product roadmaps and strategic decisions for a critical business function.

Growth Opportunities:

• Deep Specialization: Develop expertise in cutting-edge areas like agentic AI, multi-agent systems, and advanced behavioral modeling within enterprise CRM contexts.

• Leadership & Mentorship: Potential to mentor junior researchers, lead research initiatives, and contribute to the broader Quant UXR community at Google.

• Cross-Functional Influence: Grow influence across product, engineering, and design teams, driving significant product improvements and strategic direction.

• Skill Expansion: Opportunities to learn new statistical techniques, programming languages, and research methodologies relevant to AI and large-scale systems.

• Career Progression: Potential pathways to Senior Quantitative UX Researcher, Research Lead, or management roles within UX Research or related data science/product analytics functions.

📝 Enhancement Note: The growth trajectory for a Quantitative UX Researcher at Google is robust. The company invests heavily in its talent, offering numerous avenues for specialization, skill enhancement, and career advancement. The specific focus on AI and agentic systems within a critical sales platform provides a unique opportunity to become a subject matter expert in a high-demand field.

🌐 Work Environment

Office Type: Google typically offers modern, amenity-rich office environments designed for collaboration and innovation. This role is on-site, implying a need for physical presence at the Mountain View campus.

Office Location(s): Mountain View, California, United States. This is Google's headquarters, offering a vibrant campus with extensive facilities.

Workspace Context:

• Collaborative Environment: The workspace will foster close collaboration with designers, engineers, product managers, and fellow researchers, likely through a mix of shared office spaces, meeting rooms, and digital collaboration tools.

• Tools & Technology: Access to Google's proprietary research tools, robust computing infrastructure, and extensive data resources will be standard.

• Team Interaction: Opportunities for informal discussions, brainstorming sessions, and structured team meetings to share findings and align on research priorities.

Work Schedule: A standard 40-hour work week is expected, with the possibility of some flexibility in daily hours, common in tech environments. However, the on-site requirement necessitates consistent availability during core business hours for effective collaboration with teams across different time zones and functions.

📝 Enhancement Note: Working on-site at Google's headquarters in Mountain View provides an immersive environment with direct access to cutting-edge technology, resources, and a highly collaborative workforce. The emphasis on "multi-disciplinary team" collaboration suggests an open-plan or flexible office setup conducive to frequent interaction.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review your application and resume, focusing on alignment with minimum and preferred qualifications, particularly quantitative research experience, statistical skills, and programming proficiency.

• Technical Phone/Video Screen: Expect an interview with a Quantitative UX Researcher or Data Scientist to assess your technical skills, including statistical knowledge, programming ability (e.g., coding exercises in

Python/R), and understanding of experimental design.

• On-site/Virtual Loop: This typically involves several interviews (4-6) with peers, cross-functional partners (e.g., Product Managers, Designers), and hiring managers. These interviews will cover:

  • Research Case Studies: Presenting your portfolio, detailing past projects, methodologies, findings, and impact. Be prepared to discuss your role, challenges, and decisions made.
  • Problem-Solving & Analytical Skills: Hypothetical scenarios and analytical challenges related to user behavior, system evaluation, or experimental design.
  • Statistical & Programming Proficiency: Deeper dives into statistical concepts, model application, and coding skills.
  • Collaboration & Communication: Assessing your ability to work effectively with diverse teams and communicate complex ideas clearly.
  • Cultural Fit: Evaluating alignment with Google's values and research community.

• Hiring Committee Review: Your interview feedback is compiled and reviewed by a hiring committee to make a final decision.

Portfolio Review Tips:

• Focus on Impact: Clearly articulate the business or user impact of your research. Use metrics and quantifiable results whenever possible.

• Showcase Quantitative Rigor: Detail your statistical methodologies, experimental designs, and data analysis techniques. Explain why you chose specific approaches.

• Highlight Technical Skills: Include examples demonstrating proficiency in Python, R, or other relevant languages for data manipulation and analysis.

• Structure for Clarity: Organize your portfolio logically, perhaps by project type or impact area. For each project, include: problem statement, research questions, methodology, key findings, recommendations, and outcomes.

• Tailor to the Role: Emphasize experience with CRM, B2B, agentic AI, or multi-agent systems if applicable. Showcase your ability to work with complex data and systems.

Challenge Preparation:

• Statistical Concepts: Brush up on mixed-effects models, item response theory, regression analysis, Bayesian statistics, and experimental design principles.

• Programming Skills: Practice coding challenges in Python or R, focusing on data manipulation (e.g., Pandas, dplyr), statistical analysis, and visualization.

• Hypothetical Scenarios: Prepare to tackle questions like: "How would you measure the success of feature X?" or "Design an experiment to understand why users drop off at step Y."

• AI/Agentic Systems: If you have experience, be ready to discuss how you'd evaluate or research systems involving AI agents, routing, or human-in-the-loop processes.

📝 Enhancement Note: Google's interview process is known for its rigor, particularly for technical roles. A strong portfolio demonstrating both depth in quantitative methods and breadth in applied research impact is essential. Expect a thorough technical assessment and a strong emphasis on how your research has driven tangible product improvements.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (with libraries like Pandas, NumPy, SciPy, Statsmodels, Scikit-learn), R (with libraries like dplyr, ggplot2, lme4). MATLAB, C++, Java, or Go may also be relevant depending on specific team needs.

• Statistical Software: Proficiency with statistical packages and libraries for advanced modeling (mixed-effects, item response modeling).

• Data Manipulation & Analysis: Tools and languages for querying, cleaning, transforming, and analyzing large datasets.

Analytics & Reporting:

• Experimental Design Platforms: Tools for designing, launching, and analyzing A/B tests and other experiments.

• Data Visualization Tools: Tools like Matplotlib, Seaborn (Python), ggplot2 (R), or potentially internal Google visualization platforms for creating clear and impactful charts and dashboards.

• Log Analysis Tools: Experience with systems for processing and analyzing large-scale user interaction logs.

CRM & Automation:

• CRM Platforms: Direct experience with CRM systems, particularly enterprise-level platforms like Salesforce, Dynamics 365, or Google's internal CRM (for Google Ads Sales), is highly beneficial.

• Agentic AI/Multi-agent Systems: Familiarity with concepts and potentially tools/frameworks related to AI agents, workflow automation, orchestrators, and routing systems.

• Data Instrumentation: Understanding how to define and work with systems that track user events and data points within applications.

📝 Enhancement Note: The technology stack for this role is heavily focused on data analysis, statistical modeling, and programming. Proficiency in Python and R is almost a given. Experience with AI-related systems and a deep understanding of CRM functionalities are key differentiators, especially given the specific context of the Google Ads Sales platform.

👥 Team Culture & Values

Operations Values:

• User Focus: A fundamental principle at Google, ensuring all research and product decisions are driven by user needs and behaviors.

• Data-Driven Decision Making: Strong emphasis on empirical evidence, statistical rigor, and data-backed insights to inform strategy and product development.

• Innovation & Experimentation: A culture that encourages exploring new ideas, testing hypotheses, and pushing the boundaries of what's possible, especially in areas like AI.

• Collaboration & Inclusivity: Valuing diverse perspectives and fostering an environment where cross-functional teams can work together effectively to achieve common goals.

• Impact & Ownership: Taking initiative, driving projects to completion, and being accountable for the impact of one's research.

Collaboration Style:

• Cross-Functional Integration: Researchers are expected to be embedded within product teams, working hand-in-hand with Product Managers, Designers, and Engineers from ideation through launch and iteration.

• Process Review & Feedback: A culture of constructive feedback, where research methodologies and findings are discussed openly within the research community and with project teams.

• Knowledge Sharing: Encouraging the sharing of best practices, learnings, and tools within the Quantitative UX Research community at Google, often through internal forums, meetups, and documentation.

📝 Enhancement Note: The team culture at Google highly values analytical rigor, collaboration, and a relentless focus on user impact. For a Quantitative UX Researcher, this means being comfortable with complex data, adept at communicating insights to varied audiences, and contributing to a culture of continuous learning and improvement within product development.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of Agentic AI Systems: Researching and evaluating the efficacy and user experience of novel, complex AI systems (multi-agent architectures, routing) can be challenging due to their emergent behaviors and intricate logic.

• Data Instrumentation & Availability: Ensuring accurate and comprehensive data instrumentation for intricate workflows and potentially novel AI interactions may require significant collaboration with engineering.

• Balancing Rigor with Speed: In a fast-paced tech environment, balancing the need for statistically sound research with the demand for timely insights can be a constant challenge.

• Communicating Novel Concepts: Translating complex AI concepts and research findings related to agentic systems to non-expert stakeholders requires exceptional communication skills.

Learning & Development Opportunities:

• Advanced Statistical Techniques: Opportunity to deepen expertise in cutting-edge statistical modeling relevant to AI evaluation and human-AI interaction.

• Cutting-Edge Research Areas: Gain hands-on experience in the rapidly evolving field of agentic AI and multi-agent systems, a high-demand skill set.

• Cross-Functional Skill Development: Enhance collaboration and communication skills by working closely with world-class product managers, designers, and engineers.

• Mentorship Programs: Access to formal and informal mentorship within Google's extensive UX Research community, providing guidance on career growth and technical development.

• Internal Tools & Resources: Leverage Google's proprietary research platforms, data infrastructure, and learning resources to continuously enhance skills and knowledge.

📝 Enhancement Note: The challenges presented are inherent to working at the forefront of AI research within a large technology company. The growth opportunities are substantial, offering a chance to become a leader in a critical and evolving domain.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used quantitative data to significantly influence product strategy or a major product decision. What was your process?" (Focus on your role, methodology, key findings, and the impact.)

• "How would you design an experiment to measure the effectiveness of a new AI-driven routing system within a CRM? What metrics would you track and why?" (Demonstrate understanding of experimental design, CRM context, and AI evaluation.)

• "Imagine you are tasked with evaluating the quality of AI-generated responses in a multi-agent system. What statistical models or approaches would you use, and how would you address potential biases?" (Showcase knowledge of advanced statistical modeling and bias mitigation.) Company & Culture Questions:

• "Why are you interested in working at Google, specifically on the Google Ads CRM platform and its agentic AI initiatives?" (Align your interests with Google's mission and the specific role's challenges.)

• "How do you approach collaboration with Product Managers and Engineers who may have different priorities or levels of understanding regarding research?" (Highlight your communication and stakeholder management skills.)

• "How do you ensure your research findings lead to actionable improvements and measurable impact?" (Focus on your process for translating insights into outcomes.) Portfolio Presentation Strategy:

• Structure for Impact: For each case study, clearly articulate the problem, your research questions, the quantitative methods you employed (and why), key findings, the recommendations you made, and the measurable impact of those recommendations.

• Quantify Everything: Use numbers, statistics, and metrics to demonstrate the scale of the problem, the rigor of your analysis, and the success of your interventions.

• Technical Depth: Be prepared to dive into the specifics of your statistical models, experimental designs, and programming approaches.

• Storytelling: Weave a narrative around your projects that highlights your problem-solving skills, your ability to navigate complex data, and your contribution to product success.

• Focus on Agentic AI/CRM: If you have relevant experience, ensure those projects are prominently featured and discussed in detail.

📝 Enhancement Note: Preparation for this role requires a deep understanding of quantitative research methodologies, statistical modeling, programming, and the ability to apply these skills to complex AI-driven systems within a business context. The interview process will rigorously test these competencies.

📌 Application Steps

To apply for this Quantitative UX Researcher position:

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

• Curate Your Portfolio: Select 2-3 of your most impactful quantitative UX research projects that best showcase your statistical modeling, experimental design, and data analysis skills. Prioritize projects that demonstrate experience with complex systems, B2B contexts, or AI/agentic technologies if applicable. Ensure each project clearly articulates the problem, your methodology, key findings, and measurable impact.

• Tailor Your Resume: Highlight keywords from the job description, such as "quantitative UX research," "statistical modeling," "Python," "R," "experimental design," "survey research," "agentic AI," and "CRM." Quantify your achievements with specific numbers and impact metrics.

• Prepare Your Presentation: Practice walking through your portfolio case studies concisely and effectively. Be ready to answer in-depth questions about your methodology, decision-making process, and the outcomes of your research. Prepare to discuss hypothetical research scenarios.

• Research Google Ads & CRM: Familiarize yourself with the Google Ads platform and the general functions of CRM systems. Understand the challenges and opportunities in applying AI to sales and customer engagement processes.

⚠️ 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.