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Entry Level Data Labeling Analyst Jobs (NOW HIRING)

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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Entry Level Data Labeling Analyst information

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How much do entry level data labeling analyst jobs pay per hour?

As of Jul 18, 2026, the average hourly pay for entry level 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 are some typical daily tasks for an Entry Level Data Labeling Analyst, and how do they contribute to larger projects?

As an Entry Level Data Labeling Analyst, your daily tasks will often include reviewing and categorizing images, text, audio, or video datasets according to specific guidelines. You will use specialized software tools to tag or annotate data, ensuring accuracy and consistency to help train machine learning models. Your attention to detail directly impacts the quality of AI systems, making your work essential for the success of data-driven projects. Collaboration with team leads and engineers is common, as they may provide feedback or clarify labeling requirements.

What are the key skills and qualifications needed to thrive as an Entry Level Data Labeling Analyst, and why are they important?

To thrive as an Entry Level Data Labeling Analyst, you need strong attention to detail, basic computer literacy, and a high school diploma or equivalent. Familiarity with data labeling platforms, spreadsheets, and annotation tools such as Labelbox or Supervisely is often required. Diligence, consistency, and the ability to follow instructions precisely are standout soft skills in this role. These competencies ensure the accurate and efficient preparation of high-quality labeled data, which is crucial for training reliable machine learning models.

What are Entry Level Data Labeling Analysts?

Entry Level Data Labeling Analysts are professionals who tag, categorize, or annotate data such as images, text, audio, or video to help train machine learning models. Their work is crucial in ensuring that artificial intelligence systems receive accurate and well-organized information for learning and prediction tasks. Typically, these analysts use specialized software tools to label data based on guidelines provided by data scientists or project leads. This role often requires attention to detail, consistency, and the ability to follow instructions closely. Entry level positions typically do not require advanced technical skills, making it a common starting point for those interested in AI and data science fields.
More about Entry Level Data Labeling Analyst jobs
What cities are hiring for Entry Level Data Labeling Analyst jobs? Cities with the most Entry Level Data Labeling Analyst job openings:
What are the most commonly searched types of Data Labeling Analyst jobs? The most popular types of Data Labeling Analyst jobs are:
What states have the most Entry Level Data Labeling Analyst jobs? States with the most job openings for Entry Level Data Labeling Analyst jobs include:
Infographic showing various Entry Level Data Labeling Analyst job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 1% Internship, 86% Full Time, 6% Part Time, 1% Temporary, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $68,487 per year, or $32.9 per hour.
Data Labeling Analyst

Data Labeling Analyst

Welocalize, Inc.

San Diego, CA • On-site

Full-time

Posted 14 days ago


Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

361st of 451 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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