Senior UX Researcher AI

We’re looking for a Senior UX Researcher to join our UX Research Program and Practice team and work at the intersection of user research, data science, and AI‑augmented research. This is not a traditional generalist research role. You’ll split your time across four distinct work streams: developing and evaluating AI‑driven research tools, analyzing participant data to strengthen recruitment quality, building a structured, machine‑readable research repository, and conducting mixed‑methods research (including benchmarking and UX measurement).

• AI research tooling development
• Contribute to a shared repository of AI‑driven UX research tools that augment and scale the research workflow.
• Design evaluation rubrics for AI output quality – assessing AI‑generated research artifacts for things like accuracy, tone, bias, and interpretive validity.
• Iterate on tools based on measurable performance criteria, balancing automation with methodological rigor.
• Integrate research tooling into the software development lifecycle (SDLC)
• Participant database and recruitment strategy
• Query and analyze the existing participant database (SQL, R, or Python) to identify sampling biases, demographic gaps, and representation issues.
• Develop data‑driven recruitment strategies that address identified gaps.
• Design and validate surveys and screening instruments to qualify participants.
• Report findings accurately using statistical methods appropriate to the data types involved.
• Structured research repository
• Transform qualitative and quantitative research data into atomic, structured units suitable for cross‑system consumption.
• Define and maintain the schema and taxonomy that make qualitative findings machine‑readable and queryable.
• Enable downstream systems (AI tools, dashboards, cross‑functional workflows) to leverage research findings without manual retrieval.
• Ensure data quality and consistency standards across the repository.
• Mixed‑methods research
• Conduct research using existing data sources – survey backlogs, customer feedback repositories, support tickets, prior study findings – to surface patterns and generate new insights.
• Design and execute UX benchmarking studies using standardized instruments to establish baselines and measure change over time.
• Pair qualitative findings with behavioral analytics or benchmark data to triangulate insights and strengthen evidence.

• 5+ years conducting mixed‑methods UX research (qualitative and quantitative) in an enterprise product development environment.
• Bachelor's degree in a technical or human‑centered field (e.g., HCI, Data Science, Information Systems, Computer Science, Psychology) or equivalent practical experience.
• Demonstrated ability to navigate complex, ambiguous projects and adapt methods in response to new information or changing conditions.
• Experience developing, evaluating, and using AI/LLM‑based tools in a research context and think carefully about reliability and failure modes.
• Proficiency querying and analyzing large datasets using SQL, R, or Python.
• Statistical analysis fluency – ability to select and apply methods appropriate to the data type and report findings with confidence.
• Survey and screener design with attention to sampling validity; proficient in Qualtrics.
• Experience building or contributing to research repositories, taxonomies, or knowledge management systems.
• Strong understanding of research ethics, particularly participant privacy, data handling, and bias mitigation.
• Experience with secondary analysis— synthesizing findings across multiple existing studies, surveys, or feedback channels.

• Comfort working in code repositories and collaborating with engineering teams (Git, Markdown, CLI tools)
• Prompt engineering, evaluation frameworks, or AI output quality assessment experience.
• Experience with frameworks like Jobs to Be Done, mental models, or similar approaches to structuring qualitative insights.
• Experience with UX metrics programs — defining KPIs, tracking longitudinal benchmarks, reporting to stakeholders.
• Foundational understanding of RAG pipelines with vector stores, chunking strategies, and embedding models.

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