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Data Labeling Analyst Jobs (NOW HIRING)

Welocalize, Inc.

Data Labeling Analyst

New York, NY · On-site

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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Data Labeling Analyst information

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$34K

$82.6K

$136K

How much do data labeling analyst jobs pay per year?

As of Jul 8, 2026, the average yearly pay for 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.

How much are data labelers paid?

Data labeling analysts typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the labeling tasks. Some positions may offer freelance or project-based pay, with rates varying accordingly.

Is data labeling work from home?

Data labeling analysts often have the opportunity to work from home, as many companies offer remote positions for tasks like image, text, or audio annotation. These roles typically require basic computer skills and attention to detail, and they can be performed using labeling tools and software from a home environment.

What are some common challenges faced by Data Labeling Analysts, and how can they be addressed?

One common challenge for Data Labeling Analysts is maintaining consistency and accuracy when labeling large volumes of complex data, as even minor errors can impact model performance. Frequent communication with project managers and data scientists can help clarify labeling guidelines and ensure alignment with project goals. Utilizing quality assurance processes, such as cross-checking work and leveraging feedback, also supports higher accuracy. Being adaptable and open to feedback helps analysts continuously improve and meet evolving project standards.

What does a data labelling analyst do?

A data labeling analyst is responsible for reviewing and annotating data such as images, text, or videos to help train machine learning models. They use tools and guidelines to ensure data is accurately labeled, which is essential for developing reliable AI systems. Attention to detail and familiarity with data annotation tools are important for this role.

What are the key skills and qualifications needed to thrive in the Data Labeling Analyst position, and why are they important?

A Data Labeling Analyst requires strong attention to detail, excellent analytical abilities, and familiarity with data annotation and labeling processes, often supported by a bachelor's degree in a relevant field. Experience with annotation tools, basic knowledge of programming (such as Python), and familiarity with data management platforms are commonly sought after. Strong communication, time management, and the ability to work independently or as part of a distributed team are valuable soft skills. These skills ensure data accuracy and efficiency, which are critical for training reliable AI and machine learning models.

Is data labelling a good career?

Data labeling is a foundational role in machine learning and AI development, involving annotating data to improve model accuracy. It often requires attention to detail, basic technical skills, and can offer flexible schedules or remote work options. While it can be a starting point in data-related fields, career growth may involve acquiring additional skills or transitioning into more advanced roles.

What is a Data Labeling Analyst job?

A Data Labeling Analyst is responsible for annotating, categorizing, and labeling data to help train machine learning models. They work with text, images, audio, or video data, ensuring accuracy and consistency according to predefined guidelines. This role requires attention to detail, a strong understanding of data patterns, and sometimes domain-specific knowledge to improve AI performance. Analysts often collaborate with data scientists and engineers to refine labeling strategies and enhance model training.

More about Data Labeling Analyst jobs
What are the most commonly searched types of Data Labeling Analyst jobs? The most popular types of Data Labeling Analyst jobs are:
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Data Labeling Analyst jobs near you
Infographic showing various Data Labeling Analyst job openings in the United States as of July 2026, with employment types broken down into 57% Full Time, and 43% Contract. Highlights an 100% In-person job distribution, with an average salary of $82,640 per year, or $39.7 per hour.
Data Labeling Analyst

Data Labeling Analyst

Welocalize, Inc.

New York, NY • On-site

Full-time

Re-posted 4 days ago

Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

348th of 441 rated business services

Job description

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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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