Data Labeling Analyst Jobs
Data Labeling Analyst jobs are open across technology, healthcare, automotive, and e-commerce, at every level from entry-level to senior and lead, with specializations in image annotation, NLP labeling, and quality assurance. Scan the live roles below and apply to whichever ones fit.
Find Data Labeling Analyst JobsOverview
Showing 5 of 7+ Data Labeling Analyst jobs
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.
Key Responsibilities
- 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.
Required Qualifications
- 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
Desired Qualifications
- 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.
About NextGen:
NextGen Federal Systems is an innovative technology and professional services provider specializing in advanced software solutions and comprehensive mission and business support services. We work in close collaboration with our customers to truly understand their business and mission goals. Our approach is to design, build, implement, and manage solutions that measurably improve our client’s organizational performance. We have established and foster a corporate culture where we:
- Treat employees with fairness and respect regardless of their position, sexual identity, race, or tenure.
- Communicate the importance of our mission and our employees’ contributions to it, ensuring they understand how their job role contributes to the greater good.
- Openly promote and communicate our ideas for change and adaptability.
- Strive to achieve results as an organization.
- Hold employees accountable to their commitments and provide incentives that encourage positive and productive behaviors.
- Value the talents and contributions of our employees as the key factor for our success.
- Create an environment where people can engage at all levels.
- Encourage people to take risks and allow them to make mistakes.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities.
RefID: A02x
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.
Key Responsibilities
- 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.
Required Qualifications
- 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
Desired Qualifications
- 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.
About NextGen:
NextGen Federal Systems is an innovative technology and professional services provider specializing in advanced software solutions and comprehensive mission and business support services. We work in close collaboration with our customers to truly understand their business and mission goals. Our approach is to design, build, implement, and manage solutions that measurably improve our client’s organizational performance. We have established and foster a corporate culture where we:
- Treat employees with fairness and respect regardless of their position, sexual identity, race, or tenure.
- Communicate the importance of our mission and our employees’ contributions to it, ensuring they understand how their job role contributes to the greater good.
- Openly promote and communicate our ideas for change and adaptability.
- Strive to achieve results as an organization.
- Hold employees accountable to their commitments and provide incentives that encourage positive and productive behaviors.
- Value the talents and contributions of our employees as the key factor for our success.
- Create an environment where people can engage at all levels.
- Encourage people to take risks and allow them to make mistakes.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities.
RefID: A02x
See All 7 Data Labeling Analyst Jobs
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Find Data Labeling Analyst JobsData Labeling Analyst Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
What Employers Look For
The qualifications that appear most often in data labeling analyst jobs.
- Experience with annotation tools such as Labelbox, Scale AI, or CVAT
- High attention to detail and consistent inter-annotator agreement scores
- Familiarity with labeling guidelines for image, text, audio, or video data
- Ability to meet daily throughput targets while maintaining quality standards
- Basic understanding of machine learning workflows and how labeled data is used
- Associate or bachelor's degree in a relevant field, or equivalent hands-on experience
Tips for Your Data Labeling Analyst Job Search
Tailor your resume to annotation tools
List the specific labeling platforms you've used, such as Scale AI, Labelbox, or CVAT, by name. Recruiters screening data labeling analyst resumes scan for tool familiarity first, so generic phrases like 'data annotation experience' get filtered out before a human reads your application.
Quantify your labeling accuracy and throughput
Hiring managers for data labeling roles care about inter-annotator agreement scores and volume per shift, not just that you've done the work. Pull your actual accuracy rates or task completion numbers from past projects and put them directly on your resume as concrete figures.
Filter openings by the data modality you know best
Data labeling analyst roles differ sharply depending on whether the team labels text, images, audio, or video. Target postings that match your strongest modality first, since your domain-specific vocabulary in cover letters and interviews will immediately separate you from generalist applicants.
Apply early to roles that fit
Migrate Mate lists data labeling analyst openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for a paid annotation task before interviews
Many employers send a timed labeling task or quality-review exercise before the first call. Practice annotating under time pressure using the same guidelines format, such as label taxonomy documents or decision trees, that professional annotation teams actually use on the job.
Negotiate scope, not just compensation
Data labeling analyst offers often come with variable workloads tied to project cycles. During negotiation, ask about guaranteed minimum hours, how project pauses are handled, and whether the role transitions to full-time if volume increases, so you understand the real stability of the position.
Data Labeling Analyst Jobs: Frequently Asked Questions
Which companies are hiring the most data labeling analysts?
The companies hiring the most data labeling analysts right now include NVIDIA, PlusAI, and Bobyard, with the largest share of openings in California, Maryland, and Pennsylvania, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated at companies building or expanding proprietary AI datasets for computer vision, natural language processing, and autonomous systems.
How many data labeling analyst jobs are remote?
About 100% of data labeling analyst openings are fully remote or hybrid as of November 2026, making it one of the more remote-accessible roles in the AI and machine learning space. Text and audio annotation roles tend to have the highest remote availability, while positions involving specialized hardware or secure data environments are more likely to require on-site work.
How do you become a data labeling analyst?
Start by learning the fundamentals of data annotation, including how labels are structured for different data types like images, text, and audio. Practice with free or open-source annotation tools to build hands-on familiarity. Build a small portfolio of annotated samples that demonstrates your consistency and accuracy. Then apply to entry-level or contract labeling roles, which are common starting points for moving into full-time analyst positions.
Can you get a data labeling analyst job with little or no experience?
Yes, many data labeling analyst roles are structured as entry-level or contract positions specifically designed for candidates without formal experience. Employers prioritize demonstrated attention to detail and the ability to follow complex annotation guidelines over prior job history. Completing a short self-directed annotation project, contributing to an open dataset, or passing a paid trial task from a prospective employer can substitute for a formal resume entry.
What does the data labeling analyst interview process look like?
The process typically starts with a recruiter screen focused on your familiarity with annotation tools and your experience with specific data modalities. That is usually followed by a take-home or timed annotation exercise where your accuracy and consistency are evaluated against a scoring rubric. Final rounds often include a conversation with a team lead or ML engineer about how you interpret ambiguous labeling guidelines and handle edge cases.
Where can I find and apply to data labeling analyst jobs?
You can find and apply to data labeling analyst jobs on Migrate Mate, which lists current openings from across the United States. Search the roles available, find the ones that match your experience and preferred data modality, and apply directly to each listing that fits.
See All 7 Data Labeling Analyst Jobs
Find roles that match your experience and apply in just a few clicks.
Find Data Labeling Analyst Jobs