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Volunteer Data Labeling Analyst Jobs (NOW HIRING)

Welocalize, Inc.

Data Labeling Analyst

San Diego, CA

Annotate data accurately, ensuring it adheres to set guidelines. Quality Assurance and Analysis: * Conduct manual quality analysis of model results. * Recognize error patterns and report anomalies ...

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Volunteer Data Labeling Analyst information

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$34K

$82.6K

$136K

How much do volunteer data labeling analyst jobs pay per year?

As of Jun 11, 2026, the average yearly pay for volunteer data labeling analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What does a Volunteer Data Labeling Analyst do?

A Volunteer Data Labeling Analyst assists organizations by reviewing, categorizing, and tagging data—such as images, text, or audio—to help train machine learning models. Their work ensures that the data used for artificial intelligence projects is accurate and well-organized. Volunteers in this role may annotate various types of data, correct errors, and maintain quality standards according to project guidelines. This position is often remote and can be a great way to gain experience in the data science field while supporting important research and development.

What is the difference between Volunteer Data Labeling Analyst vs Data Annotator?

AspectVolunteer Data Labeling AnalystData Annotator
CredentialsBasic computer skills, sometimes training providedBasic computer skills, often on-the-job training
Work EnvironmentRemote or flexible, volunteer-basedRemote or on-site, paid position
Industry UsageUsed in AI/ML projects, volunteer platformsUsed in AI/ML, tech companies, research

The Volunteer Data Labeling Analyst and Data Annotator roles both involve labeling data for machine learning models. The main difference lies in compensation and commitment: Volunteer Data Labeling Analysts typically work on a volunteer basis, often with flexible hours, while Data Annotators are paid employees or contractors. Both roles require similar skills and are used across AI and tech industries, but the volunteer role emphasizes community contribution without monetary compensation.

What are the typical collaboration opportunities for a Volunteer Data Labeling Analyst within a project team?

As a Volunteer Data Labeling Analyst, you’ll often work closely with data scientists, machine learning engineers, and project managers to ensure that raw data is accurately categorized for model training. Collaboration typically involves regular check-ins to align on labeling guidelines, resolve ambiguities, and provide feedback on data quality. You may also participate in team meetings to discuss project progress and address any challenges encountered in the labeling process. This collaborative environment not only supports your learning but also helps the team build more robust AI models.

What are the key skills and qualifications needed to thrive as a Volunteer Data Labeling Analyst, and why are they important?

To thrive as a Volunteer Data Labeling Analyst, you need attention to detail, basic data handling skills, and familiarity with data annotation concepts, often supported by a high school diploma or equivalent. Experience with data labeling platforms, spreadsheets, and annotation tools like Labelbox or CVAT is typically required. Strong communication, reliability, and the ability to follow instructions help someone excel in collaborative and quality-focused environments. These skills ensure accurate and consistent data labeling, which is critical for developing robust machine learning models.
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Volunteer Data Labeling Analyst jobs near you
Data Labeling Analyst

Data Labeling Analyst

Welocalize, Inc.

San Diego, CA

Full-time

Posted 8 days ago

Welocalize rating

7.2

Company rating: 7.2 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

204th of 426 rated business services

Job description

If you have a Candidate Login already, but have forgotten your password please use the steps to reset your password. If you have forgotten your email login, please contact servicedesk@welocalize.com subject Workday Candidate Login

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Job Responsibilities:

The ideal candidate will have a foundational understanding of machine learning, data annotation, quality assurance, and natural language processing. They will play a pivotal role in updating our machine learning models and ensuring their efficacy.

MAIN TASKS & RESPONSIBILITIES

Machine Learning Model Updates:

  • Update training and test model databases with new or amended synthetic textual and image data.
  • Modify and refine machine learning data creation, annotation, and rating guidelines.

Model Training and Evaluation:

  • Initiate model training processes using internal tools and command-line interfaces.
  • Evaluate the performance of trained models to gauge their efficacy and readiness for deployment.

Data Management and Annotation:

  • Design and develop test and training datasets as per the criteria provided by the project manager and other full-time employees.
  • Handle data efficiently, ensuring its integrity throughout the workflow.
  • Engage in data relevance tasks, ensuring data sets are aligned with project goals.
  • Annotate data accurately, ensuring it adheres to set guidelines.

Quality Assurance and Analysis:

  • Conduct manual quality analysis of model results.
  • Recognize error patterns and report anomalies for further investigation.
  • Deliver detailed reports on findings, including aspects such as utterance quality, LLM evaluation, ASR bug tracking, and customer pain points to be reviewed by the User Experience Research team.
  • Implement basic quality control measures and ensure the reliability of processed data.
  • Utilize intermediate data analysis techniques to extract insights and inform decision-making.
  • Arbitrate discrepancies effectively, ensuring consistent data quality.

Linguistic and NLP Tasks:

  • Apply basic knowledge of natural language processing and linguistics to data processing tasks.
  • Ensure linguistic accuracy in all processed and annotated data.

REQUIREMENTS

Preferred Qualifications:

  • Bachelor's degree in Computer Science, Data Science, Linguistics or Computational Linguistics or a related field.

Experience:

  • Ability to work in a fast-paced, collaborative environment.
  • Excellent communication skills

Skills & Knowledge:

  • Familiarity with command-line tools and interfaces.
  • Strong analytical skills with the ability to identify patterns and anomalies.

Additional Information:

This role primarily focuses on English US data sets; however, familiarity with translation or multi-lingual data sets can be a plus for future projects.

Additional Job Details:

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