We are seeking a detail-oriented Data Annotation Specialist to join our team. The ideal candidate will be responsible for accurately labeling and annotating data to support the development and improvement of our machine learning models. This role requires strong attention to detail, the ability to follow instructions precisely, and a good understanding of the context in which the data is being used.

Key Responsibilities:

• Data Labeling: Accurately label and annotate various types of data (text, images, audio, video) according to specific guidelines and standards.
• Quality Assurance: Ensure the accuracy and quality of annotations by reviewing and correcting annotations as needed.
• Tool Utilization: Use annotation tools and software effectively to complete tasks.
• Collaboration: Work closely with data scientists, machine learning engineers, and other team members to understand project requirements and provide feedback on annotation processes.
• Documentation: Maintain clear and detailed documentation of annotation processes, guidelines, and any issues encountered.
• Continuous Improvement: Participate in training sessions and contribute to the development of best practices for data annotation.

• Technical Skills: Proficiency in using data annotation tools and software; basic understanding of machine learning concepts is a plus.
• Attention to Detail: Strong attention to detail and the ability to perform repetitive tasks with a high degree of accuracy.
• Communication: Excellent verbal and written communication skills.
• Problem-Solving: Ability to identify and report issues in the data annotation process.
• Team Player: Ability to work effectively in a team environment.

• Additional Requirements:

  • Adaptability: Ability to adapt to changing project requirements and priorities.
  • Time Management: Strong organizational and time management skills to meet deadlines.
  • Confidentiality: Maintain confidentiality and security of data.

Previous experience in data annotation, data entry, or a related field is preferred.