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Freelance Data Labeling Analyst Jobs (NOW HIRING)

Welocalize, Inc.

Data Labeling Analyst

New York, NY

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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Freelance Data Labeling Analyst information

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How much do freelance data labeling analyst jobs pay per hour?

As of Jul 29, 2026, the average hourly pay for freelance data labeling analyst in the United States is $32.93, according to ZipRecruiter salary data. Most workers in this role earn between $21.15 and $36.78 per hour, depending on experience, location, and employer.

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

AspectFreelance Data Labeling AnalystData Annotator
CredentialsBasic data labeling skills, sometimes certifications in data annotation toolsSimilar; often no formal certifications required
Work EnvironmentRemote, freelance projects for various clientsRemote or in-house, depending on employer
Industry UsageUsed across AI, machine learning, and data science projectsPrimarily in AI training datasets and machine learning
Search & Comparison IntentHigh overlap; both involve labeling data for AI models

Both Freelance Data Labeling Analysts and Data Annotators perform data labeling tasks essential for training AI models. The main difference lies in the freelance nature and potential project variety for Analysts, while Annotators may work more consistently within specific companies or platforms. Both roles require similar skills and are used widely in AI and machine learning industries.

What is a Freelance Data Labeling Analyst?

A Freelance Data Labeling Analyst is a professional who works independently to tag, categorize, or annotate data—such as images, texts, or audio—to help train machine learning models. These analysts play a crucial role in ensuring that artificial intelligence systems receive accurate and high-quality training data. Their work typically involves reviewing raw data and applying specific labels according to established guidelines. Freelance analysts can work remotely for various clients, often via online platforms or data annotation companies. This job requires attention to detail, consistency, and sometimes domain-specific knowledge.

What are some common challenges Freelance Data Labeling Analysts face when working with multiple clients?

Freelance Data Labeling Analysts often juggle varied guidelines, annotation tools, and project requirements from different clients. Adapting quickly to new labeling standards and software platforms is essential, as each client may have their own specifications for data quality and turnaround times. Additionally, managing communication across multiple teams and ensuring consistent delivery can require strong organizational skills and proactive time management. Building a transparent workflow and clarifying expectations with each client helps mitigate these challenges.

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

To thrive as a Freelance Data Labeling Analyst, you need strong attention to detail, data literacy, and a solid understanding of data annotation standards, often supported by a background in computer science or related fields. Familiarity with data labeling platforms, annotation tools like Labelbox or Supervisely, and sometimes knowledge of Python or SQL is valuable. Diligence, self-motivation, and the ability to follow complex guidelines set apart top analysts in this role. These skills ensure accurate, high-quality labeled datasets that are crucial for effective machine learning model training.
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Freelance Data Labeling Analyst jobs near you
Infographic showing various Freelance Data Labeling Analyst job openings in the United States as of July 2026, with employment types broken down into 53% Full Time, 6% Temporary, 35% Contract, and 6% Nights. Highlights an 94% In-person, and 6% Hybrid job distribution, with an average salary of $68,487 per year, or $32.9 per hour.
Data Labeling Analyst

Data Labeling Analyst

Welocalize, Inc.

New York, NY

Full-time

Posted 24 days ago

Welocalize rating

6.5

Company rating: 6.5 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

331st of 488 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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