Data Annotation Specialist
Insight Global
Job Description
A client of Insight Global is seeking 3 Data Annotation Specialists for an initial 1-month engagement supporting conversational AI tool validation efforts. Resources will manually review and label text-based datasets used to evaluate AI tool selection accuracy. This project will serve as a pilot, with potential for future annotation work as additional datasets become available.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:
Skills and Requirements
Strong attention to detail
Excellent written communication skills
Ability to follow detailed instructions and documentation
Comfortable performing repetitive, quality-focused work
Strong English language proficiency
Basic Excel proficiency (ability to navigate and work within spreadsheets) Manually annotate and label conversational AI datasets
Evaluate whether the correct tool or response aligns with user intent
Follow annotation guidelines consistently and accurately
Participate in frequent feedback sessions with the HP team
Escalate unclear labeling scenarios for review
Maintain quality and consistency across large volumes of data
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