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Data Annotation Project Manager Jobs in Queens, NY

Oversee data annotation projects, translating complex AI and machine learning requirements into ... project management instincts, and a bias for getting things done * Analytically rigorous and ...

Oversee data annotation projects, translating complex AI and machine learning requirements into ... project management instincts, and a bias for getting things done * Analytically rigorous and ...

... manage data sprawl while securely enabling AI adoption. Fresh off our $25 million Series A round ... You'll use Python, SQL, and agentic development tools to make annotation and QC faster, more ...

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 ...

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 ...

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 ...

Data Labeling Associate

New York, NY

$17.50 - $22.75/hr

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 ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

... AI data annotation and collection vendors * Proactively identify, track, and manage risks and ... Manage complex sourcing projects end to end: planning, execution, measurement, and optimization

... AI data annotation and collection vendors • Proactively identify, track, and manage risks and ... projects end to end: planning, execution, measurement, and optimization • Collaborate with our ...

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Data Annotation Project Manager information

See Queens, NY salary details

$17

$60

$83

How much do data annotation project manager jobs pay per hour?

As of Jul 28, 2026, the average hourly pay for data annotation project manager in Queens, NY is $60.01, according to ZipRecruiter salary data. Most workers in this role earn between $51.92 and $70.24 per hour, depending on experience, location, and employer.

How much do data annotation project managers make?

Data annotation project managers typically earn between $60,000 and $100,000 annually, depending on experience, location, and company size. They oversee annotation teams, coordinate workflows, and ensure quality standards using tools like labeling platforms and project management software.

Does data annotation actually pay?

Data annotation project managers oversee tasks where annotators are paid for labeling data used in machine learning. The pay for annotators varies depending on the platform, project complexity, and experience, with many earning hourly wages or per-task rates. The role of a project manager involves coordinating these efforts and ensuring quality, often with a salary or contract-based compensation.

What are the key skills and qualifications needed to thrive as a Data Annotation Project Manager, and why are they important?

To thrive as a Data Annotation Project Manager, you need strong project management skills, a solid understanding of data annotation processes, and experience with quality assurance, often supported by a degree in a relevant field. Familiarity with annotation tools (like Labelbox or Supervisely), workflow management platforms, and sometimes agile or PMP certification is highly beneficial. Exceptional communication, attention to detail, and leadership abilities help you effectively coordinate teams and ensure project deliverables meet quality standards. These skills are essential for managing complex annotation projects efficiently, maintaining data integrity, and supporting successful machine learning outcomes.

How hard is it to get hired by data annotation?

Getting hired as a data annotation project manager typically requires relevant experience in project management, familiarity with annotation tools, and strong organizational skills. The role often involves coordinating teams and ensuring quality standards, with some positions requiring certifications or prior experience in data labeling environments. Competition varies depending on the company and location, but demonstrating technical knowledge and management ability can improve chances of hiring.

What is the salary of data annotation manager?

The salary of a Data Annotation Project Manager typically ranges from $60,000 to $100,000 annually, depending on experience, location, and company size. They often oversee teams using annotation tools and ensure quality standards are met in data labeling projects.

What are some common challenges faced by Data Annotation Project Managers, and how can they be managed effectively?

One of the primary challenges Data Annotation Project Managers face is ensuring high-quality, consistent labeling across large and sometimes distributed annotation teams. Managing tight deadlines while maintaining annotation accuracy requires effective training, clear guidelines, and regular quality checks. Additionally, balancing communication between data scientists, clients, and annotators is crucial to align expectations and resolve ambiguities quickly. Successful managers often implement robust feedback loops, leverage annotation tools with built-in quality control features, and foster an open environment for continuous improvement.

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

AspectData Annotation Project ManagerData Labeling Specialist
CredentialsTypically requires project management experience, certifications in data management or related fieldsOften requires basic technical skills, familiarity with labeling tools, sometimes certifications in data annotation
Work EnvironmentOversees teams, manages projects, coordinates workflows in office or remote settingsPerforms labeling tasks, often in a remote or on-site environment, focused on data tagging
Employer & Industry UsageUsed by tech companies, AI firms, and data service providers for managing annotation projectsEmployed within similar industries, focusing on executing labeling tasks under supervision

The main difference is that the Data Annotation Project Manager oversees and coordinates annotation projects, ensuring quality and deadlines, while the Data Labeling Specialist focuses on executing the labeling tasks themselves. Both roles are essential in the data annotation process but differ in responsibilities and scope.

What is a Data Annotation Project Manager?

A Data Annotation Project Manager is responsible for overseeing projects that involve labeling and categorizing data, such as images, text, or audio, to train machine learning models. They coordinate teams of annotators, manage project timelines, and ensure the quality and accuracy of the annotated data. This role often acts as a bridge between data scientists, clients, and annotation teams, ensuring project requirements are met efficiently and effectively.
What job categories do people searching Data Annotation Project Manager jobs in Queens, NY look for? The top searched job categories for Data Annotation Project Manager jobs in Queens, NY are:
What cities near Queens, NY are hiring for Data Annotation Project Manager jobs? Cities near Queens, NY with the most Data Annotation Project Manager job openings:

Human Data Operations Strategist

Encord

New York, NY • On-site

Full-time

Medical, Dental, Vision, PTO

Posted 11 days ago


Job description

About us
Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.
Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.
The role
As a Human Data Operations Strategist, you will play a critical role in managing and optimising data annotation and machine learning workflows for our clients. You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models.
What you'll do
  • Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams
  • Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops
  • Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process
  • Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices
  • Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies

Who we're looking for
  • A sharp, execution-oriented operator with a consulting or AI company pedigree - you bring structured thinking, strong project management instincts, and a bias for getting things done
  • Analytically rigorous and comfortable with ambiguity - you break down complex operational challenges from first principles and build clear, actionable plans to solve them
  • Technically fluent enough to get hands-on with data - whether that's querying a database, auditing annotation outputs, or automating a workflow in Python
  • Passionate about AI and machine learning, with genuine curiosity about how data quality and operations underpin model performance
  • A natural communicator who can translate fluidly between ML engineers and non-technical clients, keeping complex multi-stakeholder projects on track
  • Entrepreneurial and collaborative - you thrive in fast-paced environments and take ownership without waiting to be told what to do

Experience requirements
  • 3-7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies
  • Proven ability to own complex, multi-stakeholder workflows end-to-end - from scoping and planning through execution, quality assurance, and iteration
  • Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued
  • Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability - ideally in a context involving human-in-the-loop workflows or structured labelling tasks
  • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions
  • Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams

Why Encord
  • Competitive salary, commission, and meaningful equity in a high-growth start-up
  • Clear, accelerated growth opportunities as the company scales rapidly
  • Strong in-person culture: 4 days/week
  • Flexible PTO to fully recharge
  • Annual learning & development budget
  • Comprehensive health, dental, and vision coverage
  • Frequent travel opportunities across the U.S., London, and Europe
  • Bi-annual company offsites, twice-weekly team lunches, and monthly socials