Microsoft Office Specialist Data Labeler

Remote Full-time
Please fill out this form to be considered: https://docs.google.com/forms/d/e/1FAIpQLSe8307orzgdMSW_RoJOW07L_qZN9rnPOjM-9g04cu-jWemRfA/viewform?usp=dialog Company Description Verita AI is at the forefront of next-generation artificial intelligence, emphasizing nuance, multimodal reasoning, and human judgment. We build high-trust data pipelines that train and evaluate cutting-edge AI models across language, vision, audio, and video. Verita—meaning “truth”—reflects our mission to create contextually rich, reliable data layers that advance the field of AI safely and transparently. Role Description We are seeking detail-oriented Microsoft Office Specialists to support data labeling and annotation projects. In this role, you will complete structured tasks within Microsoft Excel, Word, and PowerPoint while recording your screen and following precise written and visual instructions. You’ll help create high-quality labeled datasets that train and evaluate AI systems to better understand and emulate expert productivity workflows in Microsoft Office. This is a remote contract role with flexible hours. Responsibilities • Complete assigned labeling tasks accurately using Microsoft Excel, Word, and PowerPoint • Record your screen while completing each task, ensuring full visibility into your workflow • Follow detailed step-by-step guidelines to annotate, edit, and organize documents • Ensure consistency, accuracy, and completeness in all labeling work • Provide feedback on task clarity or usability when needed Qualifications • Strong proficiency in Microsoft Office (Excel, Word, and PowerPoint required) • Exceptional attention to detail and ability to follow structured instructions • Reliable access to a computer with Microsoft Office installed • Comfortable recording and submitting screen-capture videos of your work • Prior experience with data labeling, QA, or administrative tasks is a plus • Strong English communication skills Compensation: $30–$65 per hour, depending on experience and task complexity.
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