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Data Annotation Specialist
3 weeks ago
**Job Title**: Data Annotation Specialist (English and French)
**Location**:Fully Remote within the Canada (excluding Quebec)
**Employment Type**: Part Time (20 hours per week)
We are seeking a passionate and detailed-oriented Data Annotation Specialist to support the evaluation and labeling of images, search queries and other forms of data for a large enterprise client. This person should have strong critical thinking and written skills to be able to understand challenging guidelines and articulate their understanding. Ideally, this person will have past data annotation experience, but this is not a requirement.
**Required Skills**:
- Attention to Detail - Ability to consistently identify and label subtle patterns or elements.
- Basic Technical Literacy - Comfortable using annotation tools and understanding data formats (e.g., text, image, audio).
- Critical Thinking - Capable of resolving ambiguous cases based on annotation rules.
- Communication Skills - Can report edge cases or unclear instructions to team leads or developers.
- Time Management - Able to meet productivity targets without sacrificing quality.
- A Bachelor’s degree or higher in a **humanities specialization** is required. Advanced degrees are strongly preferred (Master’s or PhD)
**Bonus Skills**:
- Experience with Annotation Tools - Familiarity with platforms like Labelbox, CVAT, Prodigy, or Amazon SageMaker Ground Truth.
- Understanding of Machine Learning Concepts - Basic knowledge of how annotations impact model training and evaluation.
- Quality Assurance (QA) Experience - Skill in reviewing or validating annotations done by others.
- Adaptability to Evolving Guidelines - Comfort with changing instructions and updating work practices accordingly.
- Team Collaboration Tools Proficiency - Familiarity with tools like Jira, Slack, or Trello for task tracking and communication.
- Problem-Solving Mindset - Proactive in identifying annotation issues or proposing improvements to workflows.
Salary rates at Innodata vary depending on a wide array of factors, which may include but are not limited to the role, skill set, educational background and geographic location.
