Job Summary

The Sr. Data Annotator supports the annotation of datasets used to train AI models. This role involves ensuring the accuracy and quality of annotated data, collaborating with data scientists to meet project requirements, and mentoring junior data annotators.

Responsibilities and Duties

• Support the data annotation process, including planning,
  scheduling, and coordinating annotation tasks.
  
• Ensure the accuracy and quality of annotated data by
  following quality assurance protocols.
  
• Collaborate with data scientists and other stakeholders to
  understand annotation requirements and ensure alignment with project goals.
  
• Develop and maintain annotation guidelines and standards to
  ensure consistency across all annotated datasets.
  
• Monitor and evaluate the performance of data annotators,
  providing feedback and guidance as needed.
  
• Stay updated with the latest trends and best practices in
  data annotation and apply them to improve annotation processes.
  
• Provide training and support to data annotators on
  annotation tools and best practices.
  
• Prepare and present data annotation reports and
  documentation to senior management and stakeholders.
  
• Participate in project planning and contribute to the
  development of project timelines and deliverables.
  
• Perform other duties relevant to the job as assigned by the
  Principal Data Annotator or senior management.
  
  

Requirements

• Bachelor’s degree in Data Science, Computer Science, or a
  related field
  
• Relevant certifications (e.g., Certified Data Professional
  (CDP)) are preferred
  
• Minimum of 5 years of experience in data annotation or
  related fields
  
• Experience in data annotation projects for AI or
  technology-focused products
  
• Strong analytical and problem-solving skills
  
• Excellent communication and interpersonal skills
  
• Proficiency in data annotation tools and methodologies
  
• Strong organizational and project management skills
  
• In-depth understanding of data annotation principles and
  best practices
  
• Familiarity with AI technologies and product development
  processes
  
• Knowledge of data governance, security, and regulatory
  standards
  
• Ability to manage multiple tasks and prioritize effectively
  
• Strong attention to detail and commitment to delivering
  high-quality work
  
• Ability to work independently and as part of a team
  
• Data annotation tools (e.g., Labelbox, Prodigy)
  
• Collaboration and communication tools (e.g., Slack,
  Microsoft Teams)
  
• Data management systems (e.g., SQL, NoSQL databases)