Ortal Elfenbein

• Annotated large volumes of documents, images, and form-based data to support machine learning model training across multiple AI projects.
• Drew precise bounding boxes around form elements including text fields, checkboxes, radio buttons, and field groups using web-based annotation tools.
• Applied structured labeling taxonomies and classification schemes according to detailed project guidelines consistently achieving quality scores above 97%.
• Reviewed and validated annotations submitted by other team members to ensure labeling accuracy and consistency across datasets.
• Participated in onboarding, guideline reviews and calibration sessions to align annotation standards across distributed teams.

Managed personal task queues and met daily and weekly productivity targets while working independently in a remote setting.

• Performed Hebrew language content review and data labeling tasks for NLP and document recognition AI models targeting the Israeli market.
• Tagged key form elements including input fields, labels, section headers, and signature blocks with high precision to enhance data accuracy.
• Interpreted and classified a wide range of Israeli government banking and commercial forms to support document digitization projects.
• Collaborated with quality assurance teams to resolve edge cases and refine annotation guidelines, improving overall project quality.
• Recorded completed tasks and reported quality issues or ambiguities to project supervisors promptly, ensuring project integrity.
• Successfully completed multiple qualification assessments and language certifications required for Hebrew specific annotation projects.