Data Coordinator - (Annotation, Review & MEL)

• Annotate user queries sequentially within each session while considering previous queries and responses to maintain conversational continuity and context.
  
• Translate corrected user queries into clear and grammatically accurate English while preserving the original meaning and intent.
  
• Rephrase user queries into complete standalone English queries using the current query, chat history, and available metadata for effective answer generation.
  
• Identify and map crops mentioned in queries to the standardized crop list and assign the appropriate advisory category based on query intent and context.
  
• Ensure consistency, accuracy, completeness, and adherence to project annotation guidelines, SOPs, confidentiality, and quality standards.
  
• Review annotated sessions to validate translations, rephrased queries, crop mapping, advisory category mapping, and contextual understanding incorporated during annotation.
  
• Identify inconsistencies, ambiguities, missing information, annotation errors, and potential biases, and provide corrective feedback to improve annotation quality and dataset reliability.
  
• Ensure standardized interpretation and uniformity across the dataset, with each session handled by a single annotator and independently reviewed for quality assurance.
  
• Validate agricultural advisories, pest management protocols, and crop-related recommendations against established agronomic literature and extension advisories.
  
• Create and maintain high-quality ground-truth datasets, document evaluation decisions, and follow standardized evaluation protocols.
  
• Evaluate AI-generated responses for accuracy, relevance, completeness, and alignment with recommended agricultural practices.
  
• Identify incorrect, incomplete, or misleading AI responses, flag knowledge gaps, and provide structured feedback to support model refinement and improvement.
  
• Participate in calibration and review discussions to maintain evaluation consistency and support continuous enhancement of the AI system.
  
• Perform additional tasks related to annotation, review, evaluation, and quality assurance as assigned by the supervisor or project team.
  

Requirements

• Education: Master’s degree (Second or Third or Fourth year) or PhD (Any year) in Entomology, Plant Pathology or Agronomy from an accredited university or college. Students pursuing a degree can also participate in.
  
• Experience: Experience working with or training farmers is required.
  
• Domain Knowledge: Strong understanding of crops, common plant diseases, pest identification, and agronomic terminology. Ability to assess the correctness of crop-related annotations is essential.
  
• Tools Availability: Access to a functional laptop and a reliable internet connection is mandatory.
  
• Technical Skills: Proficient with multitasking in a split-screen setup, using Excel or Google Sheets, and performing data entry and copy-paste operations efficiently.
  
• Communication Skills: Strong verbal and written communication skills to participate in team calls, deliver feedback effectively, and coordinate with the project team.
  
• Collaboration: Ability to work effectively with geographically dispersed teams and in multicultural environments.