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Data Annotation Manager Jobs in West Virginia (NOW HIRING)

Coalition, Inc.

$200K/yr

You won't manage internal labelers - all annotation work is outsourced - but you will be the single point of accountability for whether Coalition's labeled data is accurate, consistent, and fit for ...

Wipro Limited

$65K/yr

Background in data collection, AI, data annotation, or digital operations. * Familiarity with workforce management, ticketing, or project management systems. * Experience supporting macOS ...

baymark

Patient Access Representative

Morgantown, WV · On-site

$17.75 - $22.75/hr

Maintain accurate accounts, i.e. required signatures, proper account annotation, current ... Manage revenue cycle, production logs, balances and collections for self-pay clients. * Maintain ...

baymark

Patient Access Representative

Morgantown, WV · On-site

$17.75 - $22.75/hr

Maintain accurate accounts, i.e. required signatures, proper account annotation, current ... Manage revenue cycle, production logs, balances and collections for self-pay clients. * Maintain ...

All JobsData Annotation JobsData Annotation Manager JobsData Annotation Manager Jobs in West Virginia

Data Annotation Manager information

See West Virginia salary details

$24K

$75.2K

$133.2K

How much do data annotation manager jobs pay per year?

As of Sep 11, 2026, the average yearly pay for data annotation manager in West Virginia is $75,206.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,100.00 and $97,200.00 per year, depending on experience, location, and employer.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

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

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What are some common challenges faced by data annotation managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What is the difference between Data Annotation Manager vs Data Labeling Specialist?

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in West Virginia?

The most popular types of Data Annotation jobs in West Virginia are:

What are popular job titles related to Data Annotation Manager jobs in West Virginia?

For Data Annotation Manager jobs in West Virginia, the most frequently searched job titles are:

What job categories do people searching Data Annotation Manager jobs in West Virginia look for?

The top searched job categories for Data Annotation Manager jobs in West Virginia are:

Data Annotation Manager jobs near you

Senior Manager, Machine Learning (Data Operations)

Coalition, Inc.
Real Estate • 11 - 50 employees
Learn more about Coalition, Inc.external

$200K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 25 days ago

Job description

About the role
Coalition's machine learning models are only as good as the data they're trained on. This role exists to make sure that data is right.

You'll own labeling quality and methodology across Coalition - designing annotation tasks, defining quality frameworks, and ensuring every labeled dataset meets the standard required to ship production ML models. You'll manage our labeling platform (Label Studio), work directly with ML and product teams to structure labeling programs, and oversee outsourced labeling vendors to hit quality and throughput targets.
 

This role reports to the Chief Product Officer and sits at the intersection of product, ML, and operations. You won't manage internal labelers - all annotation work is outsourced - but you will be the single point of accountability for whether Coalition's labeled data is accurate, consistent, and fit for purpose.
Responsibilities
  • Labeling quality & methodology: Define annotation guidelines, taxonomies, and edge-case protocols for each labeling program. Establish gold standard datasets, inter-annotator agreement (IAA) targets, and audit sampling processes. Identify and remediate mislabeled data in existing datasets.
  • Platform & tooling: Serve as the primary user and requirements driver for Label Studio - defining project configuration needs, workflow designs, pre-labeling pipeline requirements, and integration points with ML infrastructure. Partner with the data engineering team that builds and maintains the platform.
  • Cross-functional partnership: Work with ML engineers, data scientists, and product managers to translate model requirements into well-structured labeling tasks. Challenge teams on task design when labeling instructions are ambiguous or likely to produce unreliable labels.
  • Vendor management: Source, onboard, and manage external labeling vendors and BPOs in coordination with Coalition's operations team. Set quality SLAs, run calibration sessions, and manage feedback loops to labelers. Hold vendors accountable to accuracy, not just throughput.
  • Measurement & improvement: Define and track operational metrics - label accuracy, IAA scores, cost per label, turnaround time - and use them to drive continuous improvement. Identify opportunities for active learning, model-assisted labeling, and pre-annotation to reduce cost without sacrificing quality.
Skills and Qualifications
  • 5+ years in ML data operations, data labeling, or a related field (ML engineering, data science, or data engineering with heavy labeling exposure)
  • Deep understanding of annotation quality frameworks: IAA, consensus labeling, gold standard evaluation, error taxonomy, and calibration workflows
  • Direct experience managing labeling platforms (Label Studio strongly preferred; Scale AI, Labelbox, Prodigy, or similar acceptable)
  • Track record managing outsourced labeling vendors or BPOs for ML data production
  • Familiarity with common ML labeling tasks: text classification, NER, document extraction, intent detection
  • Comfortable working in Python and SQL; bonus if you've built tooling around labeling workflows or quality measurement
  • Strong opinions on what makes labeled data good or bad, and the willingness to push back when it's bad
  • Experience in insurance, cybersecurity, or fintech is a plus but not required
Compensation

Our compensation reflects the cost of labor across several US geographic markets. The US base salary for this position ranges from $134,400/year in our lowest geographic market up to $200,000/year in our highest geographic market. Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your recruiter can share more on target salary for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed.

Perks
  • 100% medical, dental and vision coverage
  • Flexible PTO policy
  • Annual home office stipend and WeWork access
  • Mental & physical health wellness programs (One Medical, Headspace, Wellhub, and more)!
  • Competitive compensation and opportunity for advancement

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