AI Data Quality Specialist (New Grad)

Hire Feed; Colombia; Presencial; COP 12.000.000 - 18.000.000; Jornada completa

Descripción de la vacante

Hire Feed invites you to join as Human Data Manager, New Grad in Colombia. You will help maintain high-quality labeled data used for AI model training, ensuring accuracy and alignment with guidelines.

You will collaborate with cross-functional teams to refine annotation processes, monitor data quality metrics, and document data sources, labeling rules, and quality checks across the data pipeline.

Formación

• Experience with data annotation, labeling, or quality assurance processes is preferred.
• Familiarity with basic data validation techniques and attention to detail in reviewing datasets.
• Ability to follow structured guidelines and maintain consistency in data handling tasks.
• Basic understanding of AI/ML concepts and their dependence on high-quality training data is beneficial.
• Proficiency in using spreadsheets or data management tools to track and organize information.

Responsabilidades

• Review and validate human-labeled data to ensure consistency and accuracy for AI training datasets.
• Identify and resolve discrepancies in data annotations by cross-referencing source materials and guidelines.
• Collaborate with cross-functional teams to refine annotation guidelines and improve data quality standards.
• Monitor and report on data labeling performance metrics to track progress and identify trends.
• Assist in documenting processes and maintaining records of data sources, labeling rules, and quality checks.

Conocimientos

Data annotation

Data validation

Guideline adherence

AI/ML concepts

Spreadsheets

Herramientas

Excel

Descripción del empleo

Hire Feed invites you to join as Human Data Manager, New Grad in Colombia. You will help maintain high-quality labeled data used for AI model training, ensuring accuracy and alignment with guidelines.

You will collaborate with cross-functional teams to refine annotation processes, monitor data quality metrics, and document data sources, labeling rules, and quality checks across the data pipeline.