Data Annotation Job

Job Summary:
We are looking for a meticulous Data Annotation Specialist to join our team and contribute to building high-quality datasets for machine learning models. The role involves labeling data (text, image, video, audio) following specific guidelines to ensure data accuracy, consistency, and quality. The annotated data will be used for training AI models, enabling them to make accurate predictions.Key Responsibilities:
Data Labeling: Annotate data accurately using specialized tools (e.g., images, text, speech, video).
Quality Control: Review and validate annotations made by other team members to ensure accuracy and consistency.
Follow Guidelines: Adhere to detailed annotation instructions and labeling conventions provided by project managers.
Collaborate: Work closely with data scientists and engineers to understand project goals and labeling requirements.
Feedback: Provide feedback on annotation guidelines and suggest improvements to ensure the annotation process aligns with the projects goals.
Reporting: Document progress and issues during the annotation process and report to supervisors.Required Skills:
Attention to Detail: Ability to identify subtle patterns and distinctions in the data to ensure high annotation quality.
Basic Technical Knowledge: Familiarity with data annotation tools such as Labelbox, CVAT, Amazon SageMaker Ground Truth, or similar platforms.
Communication: Ability to clearly communicate issues and feedback with the project management team.
Analytical Skills: Comfortable working with large datasets and understanding the objectives of machine learning models.Preferred Qualifications:
Experience in data labeling for specific domains (e.g., image recognition, natural language processing, autonomous vehicles).
Familiarity with AI/ML concepts and how annotated data impacts model performance.
Experience with datasets containing sensitive or confidential information and knowledge of privacy concerns.Education:
Bachelors degree in Computer Science, Data Science, Information Systems, or a related field is preferred but not mandatory.

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