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Flexible Data Annotation Analyst Jobs (NOW HIRING)

Q Analysts

Data Annotation Technician

Kirkland, WA · On-site

Data Annotation Technician Join Q Analysts and become part of a world-class organization. Q Analysts has a sixteen-years track record of providing managed services and we've partnered with some of ...

Q ANALYSTS LLC

Data Annotation Technician

Kirkland, WA · On-site +1

Q Analysts has a sixteen-years track record of providing managed services and we've partnered with ... Q Analysts is looking for Data Annotation Technicians to support Ground Truth Data Collection ...

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Flexible Data Annotation Analyst information

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$34K

$82.6K

$136K

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

As of Sep 11, 2026, the average yearly pay for flexible data annotation 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 a flexible data annotation analyst?

A Flexible Data Annotation Analyst is a professional responsible for labeling, categorizing, and tagging data—such as text, images, audio, or video—to prepare it for use in machine learning and artificial intelligence projects. The 'flexible' aspect typically means the role allows for remote work, adjustable hours, or project-based assignments. Analysts use specific tools and follow detailed guidelines to ensure data quality and consistency. This role is crucial for training accurate AI models, as well-annotated data helps improve the performance of automated systems.

What are the key skills and qualifications needed to thrive as a flexible data annotation analyst?

To thrive as a Flexible Data Annotation Analyst, you need keen attention to detail, analytical thinking, and a basic understanding of data labeling processes, often supported by a high school diploma or relevant experience. Familiarity with annotation tools such as Labelbox, Prodigy, or similar platforms, as well as basic proficiency in spreadsheet software, is typically required. Strong time management, adaptability, and clear communication skills help you deliver accurate results and work effectively with remote teams. These abilities ensure high-quality, consistent data labeling that is critical for training reliable machine learning models.

How does a flexible data annotation analyst typically collaborate with other teams to ensure data quality?

As a Flexible Data Annotation Analyst, you will frequently interact with data scientists, machine learning engineers, and project managers to clarify annotation guidelines and resolve ambiguities in the data. Collaboration often involves participating in virtual meetings, providing feedback on annotation tools, and reporting inconsistencies or uncertainties encountered during the labeling process. This teamwork ensures that annotated datasets meet project standards and contribute to high-quality machine learning outcomes. Regular communication and openness to feedback are key to success in this collaborative environment.

Can I work as a flexible data annotation analyst with no experience?

Flexible data annotation analyst roles often do not require prior experience, as training is typically provided to teach the necessary skills and tools. Basic computer literacy and attention to detail are usually sufficient to start, making it accessible for beginners interested in data labeling tasks.

Do data annotation jobs offer flexible hours?

Data annotation jobs often offer flexible hours, allowing workers to choose when they complete tasks, especially in freelance or remote roles. However, some positions may have deadlines or specific schedules depending on the employer or project requirements.

Does data annotation actually pay well?

Data annotation analysts typically earn hourly wages that are close to minimum wage or slightly above, depending on the platform and complexity of tasks. Pay rates can vary based on experience, skill level, and the employer, but generally, it is not considered a high-paying role. Many positions are freelance or part-time, which can impact overall earnings.

Is it hard to get hired for a flexible data annotation analyst?

Getting hired as a flexible data annotation analyst generally depends on having basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require extensive experience, making the role accessible to a wide range of candidates. However, competition can vary based on the employer and location, and some roles may prefer candidates with prior experience or specific technical knowledge.
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Flexible Data Annotation Analyst jobs near you
Infographic showing various Flexible Data Annotation Analyst job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 66% Full Time, 30% Part Time, and 3% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Data Annotation Engineer

Louisville, KY • On-site

Tanisha Systems
1 - 5K employees
Learn more about Tanisha Systemsexternal

Full-time

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Data Annotation Engineer

Location - Louisville, Kentucky (Day 1 onsite)


Pay Rate: Market- based on experience


We are looking for an Annotation Program Lead to manage our AI Quality Annotation Program. This individual will oversee the operational execution of human review activities that establish the gold-standard datasets used to measure conversational AI performance, safety, and compliance.
The ideal candidate combines strong program management skills with experience in quality assurance, data annotation operations, and stakeholder engagement.
  • Lead the day-to-day operation of the AI annotation program.
  • Manage a team of annotators responsible for reviewing customer conversations and AI interactions.
  • Develop annotation guidelines, procedures, and quality standards.
  • Establish calibration programs and quality assurance processes to ensure consistency across reviewers.
  • Partner with Data Scientists to create and maintain gold-standard datasets.
  • Monitor annotation accuracy, throughput, and quality metrics.
  • Coordinate reporting and deliverables for Legal, Compliance, Responsible AI, and executive stakeholders.
  • Manage annotation workflows and tooling including setting up annotation jobs, running data processing scripts, and testing annotation UIs for quality before job launch.
  • Plan capacity requirements to support seasonal increases in review volume.
  • Support future expansion into multilingual evaluation programs.
  • Identify process improvements that increase efficiency and annotation quality.
  • Bachelor's degree or equivalent experience in Linguistics/Psychology.
  • 5+ years of experience in program management, operations, quality assurance, data labeling, or related fields.
  • Experience leading teams and managing operational workflows.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.
  • Experience supporting AI, machine learning, or data annotation programs.
  • Familiarity with Responsible AI, compliance, legal review, or governance processes.
  • Experience developing operational standards and quality frameworks.
  • Experience managing vendor or contractor resources.