About the job
We are seeking a Quantitative UX Researcher who is excited to help shape the future of AI-native business applications. This is an opportunity to influence product direction by helping teams understand how customers interact with AI-powered experiences and what drives adoption, trust, usability, and customer value.
Responsibilities
- Lead quantitative and mixed-methods research for AI-powered products, including early-stage concepts and evolving customer experiences.
- Own studies from end to end, including framing research questions, defining hypotheses, selecting methods, identifying measures, analyzing results, and communicating recommendations.
- Design and run experiments, surveys, and behavioral analyses that help teams understand customer needs, product performance, and opportunities for improvement.
- Define and track success metrics related to adoption, trust, usability, comprehension, and customer value in AI-powered experiences.
- Combine findings across research, product usage, and customer feedback to generate clear insights and practical product recommendations.
- Partner closely with product, design, and engineering to support a build, test, learn approach, where evidence informs decisions throughout the product lifecycle.
Qualifications
Minimum
- 3+ years of proven success leading User Research projects with demonstrated impact experience
- Experience (end to end) with all aspects of research (study design, recruiting, moderation, analysis, reporting)
- Have a portfolio demonstrating past work experience and deliverables (e.g., study plans, reports, personas)
- Master's degree in a quantitative field
- Experience working with data and leveraging analytics to make decisions
- Experience with AI/ML technologies
- Knowledge of quantitative data analysis and statistics
- Experience with Python or R
Preferred
- Experience communicating research findings and analysis in both written and spoken channels
- Experience developing and launching V1 products
- Experience building scalable programs and repeatable scalable processes, levering various tools and methods to create scale and efficiency