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Flexible Data Labeling Analyst Jobs (NOW HIRING)

Welocalize, Inc.

Data Labeling Analyst

San Diego, CA · 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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$34K

$82.6K

$136K

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

As of Jul 13, 2026, the average yearly pay for flexible 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 is the difference between Flexible Data Labeling Analyst vs Data Annotator?

AspectFlexible Data Labeling AnalystData Annotator
CredentialsHigh school diploma or equivalent; some roles prefer certifications in data labeling or related fieldsHigh school diploma or equivalent; minimal certifications typically required
Work EnvironmentOffice or remote; collaborative with data science teamsPrimarily remote or on-site; focused on labeling tasks
Industry UsageUsed across AI, machine learning, and data science projectsCommonly used in AI training data preparation

The Flexible Data Labeling Analyst and Data Annotator roles both involve data labeling tasks, but the analyst often has broader responsibilities, including quality control and process improvement, while the annotator focuses mainly on labeling data. The analyst may require additional skills in data management and communication, making their role more strategic within data projects.

What are Flexible Data Labeling Analysts?

Flexible Data Labeling Analysts are professionals who annotate, categorize, and tag data—such as images, audio, or text—according to specific guidelines, often as part of training data for machine learning models. The 'flexible' aspect usually refers to the ability to work remotely, set variable hours, or handle diverse types of data projects. Their work is crucial for ensuring that artificial intelligence systems can learn from accurately labeled datasets. This role requires attention to detail, basic technical skills, and sometimes familiarity with the subject matter being labeled. Flexible Data Labeling Analysts may work on a freelance, contract, or part-time basis.

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

To thrive as a Flexible Data Labeling Analyst, you need strong attention to detail, analytical skills, and a solid understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with data annotation tools, spreadsheets, and sometimes basic programming or scripting languages is often required. Excellent communication, adaptability, and time management are crucial soft skills for handling varied projects and meeting quality standards. These skills ensure accurate data labeling, which is vital for training reliable AI and machine learning models.

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

Flexible Data Labeling Analysts often encounter challenges such as maintaining high accuracy while working with large volumes of data, adapting to different labeling guidelines across projects, and managing time effectively when working remotely or on a flexible schedule. To succeed, it's important to develop strong attention to detail, regularly review project instructions, and communicate proactively with team leads or project managers. Utilizing collaboration tools and participating in team check-ins can also help ensure that questions are addressed quickly and consistent standards are maintained across the team.
What cities are hiring for Flexible Data Labeling Analyst jobs? Cities with the most Flexible 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 Flexible Data Labeling Analyst jobs? States with the most job openings for Flexible Data Labeling Analyst jobs include:
Flexible Data Labeling Analyst jobs near you
Data Labeling Analyst

Data Labeling Analyst

Welocalize, Inc.

San Diego, CA • On-site

Full-time

Re-posted 8 days ago

Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

357th of 449 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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