Senior Quantitative Ux Researcher, Merchant Shopping

Google Google · Big Tech · Zürich, Switzerland +1

This role is for a Senior Quantitative UX Researcher focused on understanding user behaviors and needs for merchant shopping products. The role involves conducting primary research, analyzing data, and developing metrics to influence product strategy. A key aspect is developing AI-integrated workflows for UXers and leveraging AI/ML knowledge to inform research. The role is not directly building AI models but uses AI/ML concepts and tools.

What you'd actually do

  1. Contribute significantly to our Quantitative User Experience Researcher (UXR) program in alignment with business priorities with a focus on developing AI-integrated workflows for UXers, substantively understanding user behaviors across a large spectrum of merchants from SMBs to large retailers across a wide variety of surfaces.
  2. Influence product strategy and user experience through sound data-driven recommendations within compelling data visualizations and narratives that can travel widely across the organization.
  3. Drive understanding of merchant users and their needs at scale by leveraging existing work from the UXR team and through new initiatives and program ownership.
  4. Develop metrics and lead the measurement of the user experience in our products in collaboration with data science.
  5. Contribute to our Quantitative UXR team learning and helping elevate the quantitative research craft across the broader team of UXRs.

Skills

Required

  • Human-Computer Interaction
  • Cognitive Science
  • Statistics
  • Psychology
  • Anthropology
  • Quantitative User Experience Researcher (UXR)
  • data-driven recommendations
  • data visualizations
  • data science
  • cleaning, joining, and analyzing datasets
  • Python
  • R
  • MATLAB
  • artificial intelligence
  • Machine Learning (ML) models
  • ML infrastructure
  • natural language processing
  • deep learning

Nice to have

  • Master's degree or PhD in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, or related fields
  • qualitative and quantitative research methods
  • analysis techniques
  • technical discussions
  • Passion for AI technology

What the JD emphasized

  • artificial intelligence, Machine Learning (ML) models, ML infrastructure, natural language processing or deep learning