Data Labeller

Job Details:

• Role title: Data Labeler 
• Job Location: Complete remote work
• Duration of the Contract: 1 Month
• The client will provide training as per the project
• Project start date: Immediate
• Working days: Monday to Friday, 8 Hrs a day/40 hours per Week, Business Hours 
• Candidates need to use their own laptop for the work

Job Summary: 

The Document Intelligence (Doc Intel) Data Labeler will focus on labeling document content for AI-driven document processing projects. This involves annotating key information such as text fields, tables, forms, and document structures to enable machine learning models to extract, classify, and interpret document data effectively. The role is crucial for enhancing AI models used in document intelligence systems, such as invoice processing, contract analysis, or form extraction. 

Key Responsibilities: 

• Label and annotate various document types (e.g., invoices, contracts, receipts, forms, legal documents) to train machine learning models for document intelligence. 
• Identify and tag key elements such as text fields, headers, tables, signatures, and checkboxes in documents. 
• Annotate structured and unstructured data, ensuring proper classification, extraction points, and segmentation. 
• Follow project-specific labeling guidelines that may involve identifying specific data points like dates, amounts, entities, and other critical information. 
• Ensure the integrity of labeled data, maintaining consistency and accuracy throughout the labeling process. 
• Meet productivity targets while adhering to strict quality standards for accurate data labeling. 

Required Skills and Qualifications: 

• Good to have - Experience in data labeling for document intelligence projects (invoices, contracts, legal documents, etc.). 
• Good to have - Familiarity with OCR (Optical Character Recognition) technology and its application in document processing. 
• Ability to understand and work with complex document structures, including tables, nested forms, and multi-page documents. 
• Strong attention to detail and the ability to follow specific annotation guidelines for document-based data labeling. 
• Good to have - Basic understanding of data extraction processes and how labeled data is used in training AI models for document interpretation.