Remote Data Labeling Specialist (Contract)

Crossing Hurdles; Indonesia; On-site; IDR 733,286,000 - 1,955,429,000; Part time

Job summary

Crossing Hurdles is seeking a Generalist for Data Annotation on a contract basis. This remote role requires 10-40 hours per week with an hourly compensation of $30 to $80. You will analyze and annotate various data types, ensuring accurate labels and adherence to project guidelines.

Ideal candidates demonstrate strong communication, meticulous attention to detail, and the ability to work independently while adapting to evolving requirements across tasks and datasets.

Qualifications

• Experience in data annotation or related field.
• Clear written and verbal communication abilities.
• Strong attention to detail and commitment to accuracy.
• Flexibility to adapt to evolving project guidelines and task types.
• Ability to work independently and manage deliverables remotely.

Responsibilities

• Analyze and annotate a variety of data types according to detailed project guidelines.
• Apply critical thinking to ensure data labels are accurate, consistent, and relevant.
• Collaborate with project leads to resolve ambiguities and clarify annotation standards.
• Document observations, edge cases, and potential improvements in annotation procedures.
• Contribute to the ongoing optimization of annotation processes by providing feedback and suggestions.
• Maintain a high standard of quality and attention to detail throughout all assigned tasks.

Skills

Attention to detail

Communication skills

Independent work

Remote work capability

Job description

Crossing Hurdles is seeking a Generalist for Data Annotation on a contract basis. This remote role requires 10-40 hours per week with an hourly compensation of $30 to $80. You will analyze and annotate various data types, ensuring accurate labels and adherence to project guidelines.

Ideal candidates demonstrate strong communication, meticulous attention to detail, and the ability to work independently while adapting to evolving requirements across tasks and datasets.