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David Careers

User Study Researcher II

Posted 35 days ago

mindlanceSunnyvale (CA)
Human Factors Engineers and ErgonomistsMarketing Research and Public Opinion Polling

SENIORITY

Junior

SALARY

$70.14 / hour

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About the role

Job-ID29216641Reference26-25054 Salary$70.14 / hour
Job Summary: At Client, we strive to achieve surprise and delight for every one of our millions of customers around the world. We are currently looking for a contractor to join the Interaction & Human Factors team to support data collection and data labeling/validation for development of sensor-based interactions. An ideal candidate for this role will have outstanding interpersonal communication skills, high attention to detail, and a passion for conducting impactful research in support of creating better user experiences.
Description:
  • Manage recruitment and scheduling of qualified candidates based on study requirements.
  • Facilitate, conduct, and lead in-person user studies
  • Prepare, troubleshoot, and maintain study specific equipment for data collection efforts
  • Assist with development of instructional materials as needed
  • Develop and distribute surveys, review and collate survey responses and other feedback
  • Set up and break down study session areas
  • Engage and communicate with participants
  • Ensure appropriate participant compliance with protocol
  • Document session issues
  • Manage participant data including transferring data from test devices or session notes to secure storage
  • Assist with data review and clean-upValidate auto-labeled data and machine-generated transcripts against source recordings, correcting errors and flagging recurring or systematic issues for the team
  • Perform manual annotation of data (audio, video, sensor, and/or text) according to established labeling guidelines when auto-labeling is not available or requires human review
  • Use LLM-based tools, where appropriate, to accelerate labeling, transcript review, and preliminary analysis, applying human judgment to verify and QA model-generated output
  • Track labeling/annotation quality and consistency, and contribute feedback to refine labeling guidelines and workflows
Key Qualifications:
  • Qualitative user research skills including interviews and usability testing
  • High attention to detail & great note taking skills
  • Experience reviewing, validating, and correcting labeled data and transcripts, with a consistent eye for accuracy and edge cases
  • Ability to perform manual data annotation against defined labeling guidelines when automated labels are unavailable or unreliable
  • Knowledge of ergonomics or usability of consumer products
  • Comfortable with executing commands in Terminal
Preferred Skills:
  • Familiar with motion capture hardware & software.
  • Experience with quantitative analysis tools or languages such as Python, R, MatLab, or JMP.Experience validating auto-labeled data and machine-generated transcripts (e.g., speech-to-text, activity/event detection)
  • Familiarity with data annotation tools and workflows, and the ability to interpret and apply labeling schemas/guidelines
  • Experience using LLM-based tools to assist with data labeling, transcript review, or qualitative analysis (e.g., drafting annotations, summarization, categorization, or QA of model output)
Education & Experience:
  • Bachelor's degree required.
  • Preferred disciplines: Human Computer Interaction, Human Factors, Mechanical Engineering, Psychology, Marketing, Computer Science, or other related discipline.
  • Additional Credentials – (Preferred credentials particularly for those who do not have a Bachelors major in related discipline.)Master's degree in related discipline
  • Completion of certificate, training, or "boot camp" program for UX research, human-computer interaction, or human subjects research.1-3 years of hands-on work in human factors, industrial engineering, cognitive engineering, experimental psychology, and/or human-computer interaction.
  • Hands-on experience with data annotation, transcript validation, or dataset QA in a research, ML, or operations context

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