Quantitative UX Researcher, Customer Engagement

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.

• 6 years of experience in product research in an applied research setting, or similar.

• Experience in programming languages used for data manipulation and computational statistics (e.g., Python, R, MATLAB, C++, Java, or Go).

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

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

Preferred qualifications:

• Master's degree or PhD in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, or related field.

• 5 years of experience conducting UX research on products and working with executive leadership (e.g., Director level and above).

• 3 years of experience managing projects, and working in a large, matrixed organization.

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

• Experience evaluating multi-agent architectures, agentic workflows, orchestrators, or subagent routing systems.

About the job:

At Google, we "Focus on the user and all else will follow." As a Quantitative User Experience Researcher (Quant UXR), you make this possible. You will join a multi-disciplinary team, collaborating closely with Engineering and Product Management to create industry-leading, innovative products.

You will drive impact at all stages of development by investigating user behavior through empirical methods like logs analysis, survey research, and regression. We value diverse educational experiences—from Computer Science to Psychology—and require a blend of behavioral research design, statistical proficiency, and programming skills to uncover actionable insights. Beyond the work, you will grow within a supportive Quant UXR community offering mentorship, regular meetups, and exclusive internal tools to help you thrive.

Our Customer Relationship Management (CRM) platform powers Google Ads Sales. As we transform the platform with agentic AI, we are building intelligent workflows that route, synthesize, and resolve complex seller and advertiser tasks across general and specialized agents. In this role, you will define the measurement frameworks and behavioral models behind how users interact with multi-agent systems. You will help answer critical platform-level questions surrounding routing efficiency, AI quality calibration, and human-in-the-loop patterns to improve the overall product experience.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities:

• Define and measure quantitative UX goals and metrics in collaboration with designers, qualitative researchers, data scientists, engineers and program managers.

• Design and analyze human and synthetic evaluation systems for multi-agent workflows, applying crossed rater/item statistical models to calibrate routing quality and mitigate rater bias.

• Design, analyze, and interpret A/B experiments to measure the impact of product changes on user behavior and key metrics.

• Partner with designers, qualitative researchers, data scientists, engineers, and program managers to define user-centered success metrics and ensure appropriate data instrumentation.

• Translate complex findings into actionable insights and communicate them effectively to cross-functional stakeholders to drive research that improves the user experience.