Data Labeling Specialist
NextGen Federal Systems
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- Location
- Aberdeen, MD
- Work style
- On-site
- Job type
- Full time
- Posted
- Oct 29
- 1 day ago
Career path
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About the company
NextGen Federal Systems
Information technology and services · 201 to 500 employees
What you'll do
- Annotate and classify operational datasets, including specific modalities, using approved taxonomies, metadata standards, and task-specific instructions.
- Prepare and organize labeled datasets for use within existing AI platforms and applications.
- Review annotations for accuracy, completeness, consistency, and adherence to approved labeling conventions.
- Identify, document, and resolve data discrepancies, ambiguous records, duplicate entries, and inconsistent labels; elevate unresolved cases to appropriate subject matter experts.
- Coordinate with technical teams and functional subject matter experts to clarify labeling criteria and data interpretation.
- Perform first and second-pass quality reviews using defined sampling plans, acceptance criteria, and annotation-agreement measures; document findings and complete required rework.
- Maintain data labeling records, change history, quality findings, and supporting documentation to preserve traceability.
- Support the refinement of annotation instructions, review checklists, and repeatable data quality workflows.
- Handle data in accordance with applicable government security, access, and information management requirements.
What you need
- Bachelor’s degree in Information Systems, Data Analytics, Computer Science, or a related discipline; equivalent relevant experience may be considered.
- 3–5 years of relevant experience in data labeling, data annotation, data quality assurance, data management, or comparable data operations.
- Demonstrated experience reviewing, categorizing, and validating structured or unstructured datasets.
- Ability to apply labeling guidelines, metadata conventions, and documented quality standards consistently.
- Experience identifying data discrepancies and documenting corrective actions.
- Proficiency with spreadsheet applications and common data organization tools.
- Strong attention to detail, analytical judgment, and organizational skills.
- Ability to work independently while coordinating with technical teams and nontechnical subject matter experts.
- Strong written and verbal communication skills.
- Active Security Clearance Required: Secret or higher
- Experience supporting DoD or other government data operations programs.
- Familiarity with data annotation or labeling platforms such as Label Studio, CVAT, or comparable tools.
- Experience preparing data for use in existing AI-enabled applications or operational analytics platforms.
- Familiarity with metadata management, controlled vocabulary, taxonomies, and dataset versioning.
- Experience with quality sampling, annotation agreement checks, and repeatable data review processes.
- Familiarity with CSV, JSON, SQL, or other common data formats and query tools.
- Experience working in controlled-access or secure government computing environments.
Key skills
data annotationdata quality assurancedataset organizationlabeling standardsdata requirements
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Read the full posting here
NextGen Federal Systems is seeking a detail-oriented Mid-Level Data Labeling Specialist to support Artificial Intelligence Operations (AIOPS) initiatives within the Next Generation Command and Control (NGC2) environment. This role will focus on preparing, annotating, categorizing, and validating structured and unstructured data for use within existing AI platforms and operational applications.
AIOPS supports the operational adoption and use of AI-enabled capabilities across NGC2. Accurate, consistently organized, and traceable data helps ensure these existing platforms can effectively support operational workflows and user requirements.
The ideal candidate will have practical experience in data annotation, data quality assurance, and dataset organization, with the ability to apply defined labeling standards independently. This role requires collaboration with technical personnel, functional subject matter experts, and government stakeholders to clarify data requirements and resolve quality issues.