ZipRecruiter

Log In

1

Flexible Data Labeling Analyst Jobs (NOW HIRING)

Welocalize, Inc.

Data Labeling Analyst

San Diego, CA

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 ...

next page

Showing results 1-20

All JobsData Labeling Analyst JobsFlexible Data Labeling Analyst Jobs

Flexible Data Labeling Analyst information

See salary details

$34K

$82.6K

$136K

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

As of Jun 11, 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.

How much do data labelers typically earn?

Data labelers typically earn between $10 and $20 per hour, depending on experience, complexity of tasks, and the platform they work on. Some roles may offer project-based pay or bonuses for accuracy and speed.

Is AI replacing data analysts?

AI is automating certain tasks within data analysis, such as data cleaning and pattern recognition, but it does not fully replace data analysts. Data analysts, including those in roles like flexible data labeling, are essential for interpreting AI outputs, making strategic decisions, and handling complex or unstructured data that require human judgment and domain expertise.

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.

Can I be a data analyst in 3 months?

Becoming a data labeling analyst typically requires basic skills in data annotation tools and attention to detail, which can be developed in a few weeks. However, transitioning to a full data analyst role usually takes several months of learning in data analysis, statistics, and relevant software like Excel or SQL, making a three-month timeline feasible for entry-level tasks but unlikely for a full analyst position without prior experience.

Is data labelling a good career?

Data labeling is a common entry-level role in data annotation and machine learning workflows, requiring attention to detail and familiarity with labeling tools. It offers flexible schedules and can serve as a stepping stone to more advanced data science or AI positions, but it often involves repetitive tasks with limited long-term growth potential. Success in this career depends on developing related skills such as understanding data quality standards and using annotation software.

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

Full-time

Posted 8 days ago

Welocalize rating

7.2

Company rating: 7.2 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

204th of 426 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

When creating your Workday account and entering personal information like name, address, please do not use ALL CAPS.

Thank you!

NOTICE:For Privacy Policy please review here

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:

Apply