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Data Labeling Analyst Ii 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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How much do data labeling analyst ii jobs pay per year?

As of Jul 7, 2026, the average yearly pay for data labeling analyst ii 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 Data Labeling Analyst II do?

A Data Labeling Analyst II is responsible for accurately categorizing and annotating large datasets, which are often used to train machine learning models. This role involves reviewing data such as images, audio, text, or videos and applying predefined tags or labels according to specific guidelines. A Data Labeling Analyst II may also help improve labeling processes, provide feedback on data quality, and support junior analysts. Their work ensures that AI and data-driven technologies can learn effectively from high-quality, well-labeled data.

How does a Data Labeling Analyst II typically collaborate with data scientists and machine learning engineers during projects?

As a Data Labeling Analyst II, you will frequently work closely with data scientists and machine learning engineers to ensure datasets are accurately annotated for model training and validation. Collaboration often involves clarifying labeling guidelines, providing feedback on ambiguous cases, and adjusting annotation strategies based on project goals. Regular communication and review sessions help maintain consistency and high-quality data, which are vital for successful machine learning outcomes. This teamwork also offers opportunities to learn more about the end use of labeled data and to contribute ideas that improve overall data processes.

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

A Data Labeling Analyst II should possess strong attention to detail, familiarity with data annotation processes, and a background in computer science or a related field. Proficiency with data labeling tools such as Labelbox, Supervisely, or CVAT, as well as experience with database management systems, is typically required. Excellent communication, problem-solving abilities, and adaptability help analysts efficiently collaborate and manage evolving project requirements. These skills ensure the creation of high-quality labeled datasets, which are essential for training accurate machine learning models.
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Data Labeling Analyst Ii jobs near you
Infographic showing various Data Labeling Analyst Ii 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 $82,640 per year, or $39.7 per hour.
Data Labeling Analyst

Data Labeling Analyst

Welocalize, Inc.

New York, NY

Full-time

Posted 2 days ago

Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

345th of 437 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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