Principal Quantitative User Experience Researcher, AI

Principal Quantitative User Experience Researcher, AI

Location: New York, Austin, Los Angeles, San Jose, San Francisco, Seattle, with other EG Locations up for discussion.

Introduction to the Team

The Product Team creates high-quality end-to-end experiences for travelers, partners, and Expedia Group. Our focus on customer-centric innovation enables us to develop products that build loyalty and repeat business. We partner closely with teams across Expedia Group to drive growth and achieve results for our customers and the company.

At Expedia Group, we believe the best AI products and systems are built with humans at the center. Our User Experience Research team partners across Product, Data Science, AI/ML, and Technology to ensure that as we build more intelligent, automated, and personalized travel experiences, we never lose sight of the traveler's needs.

We are looking for a Principal Quantitative UX Researcher to help lead that research — defining how we measure human experience with AI across agentic systems, intelligent recommendations, generative features, and the evolving relationship between travelers and automation.

We're seeking someone with a creative streak, a love for science, and the rigor to build measurement frameworks that hold up under pressure. If you are a problem solver who finds just as much joy in a well-designed survey as in a well-tuned model, this could be the role for you.

In This Role, You Will:

• Define the quantitative research strategy for AI-powered product areas, establishing how we measure the quality, trust, and effectiveness of intelligent systems at scale.
• Design and execute large-scale surveys, behavioral studies, and log-data analyses — linking attitudinal data from surveys to behavioral signals from product logs to generate integrated insights.
• Build and validate measurement frameworks — including psychometric instruments, experience metrics, and AI evaluation rubrics — that apply statistical rigor to challenges like LLM output quality, human-in-the-loop assessment, and benchmark validation.
• Write and maintain complex SQL queries and Python or R scripts to access, clean, analyze, and build scalable datasets that track AI quality and experience outcomes over time.
• Partner with other Researchers, AI/ML scientists, Data Science, Product, and Design to embed human-centered measurement into AI development workflows and evaluation pipelines.
• Mentor researchers across the team and contribute to thought leadership in AI evaluation, raising the bar for quantitative rigor both internally and in the broader community.

Minimum Qualifications:

• Master's degree or PhD in Human-Computer Interaction (HCI), Computer Science, Statistics, Psychology, or a related field — or equivalent professional experience.
• 8+ years with no advanced degree, or 5–10 years with a suitable advanced degree.
• Expert in quantitative research methods: survey design and psychometrics, experimentation, key driver analysis, and hypothesis testing.
• Expert-level SQL skills and expert-level proficiency in Python or R (or both) for statistical analysis, modeling, and data visualization.
• Deep experience connecting survey-based attitudinal data to behavioral log data to generate integrated insights.
• Demonstrated understanding of AI/ML systems — including how large language models, recommendation systems, or agentic workflows function — and the ability to design research that evaluates them from a human perspective.
• Strong ability to define and operationalize metrics that are scientifically valid and meaningful to product and engineering partners, with the communication skills to make them land.

Preferred Qualifications:

• Experience designing or contributing to AI evaluation frameworks, including human evaluation protocols, LLM-as-judge validation, or automated benchmark quality assessment.
• Familiarity with psychometric validity frameworks (construct validity, criterion validity, reliability) and their application to AI output quality measurement.
• Experience working on generative AI, conversational AI, or agentic product experiences.
• Background working within or alongside AI/ML teams, not just researching their outputs.
• Experience with survey platforms (e.g., Qualtrics) and site intercept methodology.
• Contributions to the research or AI community via publications, conference presentations, or open-source work.