Principal Data Annotator

The Principal Data Annotator oversees the annotation of datasets used to train AI models. This role involves managing the data annotation process, ensuring the accuracy and quality of annotated data, and collaborating with data scientists to meet project requirements.

• Manage the data annotation process, including planning,scheduling, and coordinating annotation tasks.
• Ensure the accuracy and quality of annotated data by developing and implementing 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 toensure 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 indata annotation and apply them to improve annotation processes.
• Provide training and support to data annotators onannotation 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 theHead of AI Products & Development or senior management.

• Bachelor’s degree in Data Science, Computer Science, or arelated field
• Relevant certifications (e.g., Certified Data Professional(CDP)) are preferred
• Minimum of 8 years of experience in data annotation orrelated fields
• Experience in managing 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 andbest practices
• Familiarity with AI technologies and product developmentprocesses
• 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, No
  
  SQL databases)