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Flexible Data Labeling Analyst information

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$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.
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Flexible Data Labeling Analyst jobs near you
Croatian Data Labeling Analyst(Speech & Voice)

Croatian Data Labeling Analyst(Speech & Voice)

Welocalize

New York, NY • On-site

$26 - $28/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 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

Overview

Welo Data is looking for detail-oriented and reliable individuals to join our team as Data Labeling Analysts, supporting speech and voice AI systems.

This is a high-impact production role focused on building the datasets that power real-world AI systems. You’ll be working with audio, speech, and language data — helping ensure models are trained on accurate, well-structured, and representative inputs.

While this role is more execution-focused than evaluation-heavy roles, it still requires strong judgment, attention to detail, and consistency. The work sits at the intersection of language, data, and AI systems — where precision and discipline matter at scale.

We’re looking for people who are dependable, focused, and take pride in producing high-quality work, even across repetitive workflows.

Project Details
  • Job Title: Data Labeling Analyst
  • Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)
  • Hours: Full-time, 40 hours per week
  • Employment Type: W2 Full-Time Employee
  • Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship)
  • Pay Rate: $26 - $28/hour

Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: New York City, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, or Burlingame. Please only apply if you meet this location requirement.

What You'll Do
  • Execute high-volume data labeling and annotation tasks across speech and voice datasets
  • Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale
  • Work with audio and language data, including transcription, categorization, and tagging
  • Maintain strong throughput while meeting quality expectations
  • Escalate unclear or ambiguous cases appropriately
  • Adapt to evolving guidelines and workflows as systems and requirements change
  • Support baseline data production needs for AI training pipelines
  • Contribute to team calibrations and quality alignment sessions
What We're Looking For
  • Native-level fluency in Croatian
  • Strong written communication skills and language fundamentals
  • 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field.
  • Ability to follow detailed instructions and apply guidelines consistently
  • High attention to detail and ability to maintain accuracy in repetitive tasks
  • Comfort working in structured, process-driven environments
  • Ability to manage time effectively and maintain steady output
  • Willingness to ask questions and escalate when needed
  • Basic familiarity with AI, speech technology, or language data is a plus
Benefits
  • Paid Vacation: 6 days
  • Paid Company Holidays: 2 days (Memorial Day and Labor Day)
  • Paid Sick Leave: accrued per applicable state law and company policy
  • Medical, Dental, and Vision Insurance (eligibility applies)
  • Health Savings Account (HSA)
  • 401(k) Retirement Plan
  • Employee Assistance Program
  • Additional voluntary benefits (life, accident, critical illness, etc.)

Onsite Perks (where applicable):
Free breakfast, lunch, and dinner
Stocked micro-kitchens with snacks and beverages
Commuter benefits, including shuttles and bike-to-work options
Unique campus features depending on location