We are hiring for one of our clients, a global leader in the Data Infrastructure and Analytics industry, seeking a Human Data Manager to work on a full-time, fully remote basis overseeing data collection, validation, and management processes that ensure accuracy and usability for downstream AI applications.
About the Role — AI Training Data Management
Core Scope: Overseeing data collection, validation, and management processes
Industry Context: Supporting a global leader in Data Infrastructure and Analytics
Compensation: $40 – $60 per hour
Work Format: Fully remote, work from anywhere
Core Tools: Data labeling platforms such as Label Studio or Prodigy
 Career Impact & Remote Data Management Opportunity
Total Flexibility: Fully remote role, work from anywhere globally
Technical Ownership: Maintain and update human-annotated datasets used for AI model training
Cross-Functional Collaboration: Work with cross-functional teams to resolve annotation discrepancies
Career Growth: Contribute to scalable data solutions improving AI model performance across applications
Position Overview
This role involves overseeing data collection, validation, and management processes to ensure accuracy and usability for downstream applications. The Information Manager maintains and updates human-annotated datasets used for training and evaluating AI models, working within a global Data Infrastructure and Analytics organization.
Why This Role Matters:Â As Information Manager, you maintain and update human-annotated datasets that directly power AI model training and evaluation, review and validate data labels to ensure consistency and quality across datasets, collaborate with cross-functional teams to identify data requirements and resolve annotation discrepancies, document data collection methodologies, labeling guidelines, and quality control procedures, monitor data pipeline performance and implement improvements to enhance efficiency, and contribute to scalable data solutions that improve AI model performance across multiple applications, all from a fully remote, work-from-anywhere setup.
Key Responsibilities
Dataset Maintenance & Validation
- Maintain and update human-annotated datasets used for training and evaluating AI models
- Review and validate data labels to ensure consistency and quality across datasets
Cross-Functional Collaboration
- Collaborate with cross-functional teams to identify data requirements and resolve annotation discrepancies
Documentation & Process Improvement
- Document data collection methodologies, labeling guidelines, and quality control procedures
- Monitor data pipeline performance and implement improvements to enhance efficiency
🎓 Qualifications & Requirements
Essential Skills
- Experience with data annotation, validation, or quality assurance processes
- Familiarity with spreadsheets, databases, or data management platforms
- Attention to detail and ability to follow structured guidelines for data processing tasks
- Strong written communication skills for documenting processes and guidelines
Preferred Skills
- Proficiency in data labeling tools, such as Label Studio or Prodigy
About the Opportunity
This role offers a unique opportunity to work with a global leader in the Data Infrastructure and Analytics industry, contributing to the development of high-quality AI training datasets. The position supports scalable data solutions that improve model performance across multiple applications, hiring is conducted by a staffing partner on behalf of the end client, and candidates are welcomed regardless of background, experience, or prior employment history, with applications reviewed solely on demonstrated technical ability and qualifications.
Career Excellence: Build specialized AI training data management experience with a global Data Infrastructure and Analytics leader, in a fully remote, well-compensated role.
Who Should Apply?
- Data Annotation & QA Specialists:Â Experienced validating and labeling structured datasets
- AI Training Data Professionals:Â Familiar with tools like Label Studio or Prodigy
- Remote Data Managers:Â Comfortable working fully remotely across time zones
- Detail-Oriented Documentation Specialists:Â Skilled in writing clear labeling guidelines and procedures
- Cross-Functional Collaborators:Â Comfortable resolving annotation discrepancies across teams
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