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Data Annotation Project Manager Jobs in Woonsocket, RI

Data Analytics Project Manager

Boston, MA · On-site

$56.25 - $76/hr

Data Analytics Project Manager Onsite 3-4 days per week/WFH 1-2 days per week Company Summary: As a national leader in the construction industry, we are redefining what it means to build. We ...

Data Center Project Manager

Milford, MA · On-site

$132K/yr

The data center market is booming. If you are a strong project manager, who is highly organized and wants to support an iconic brand of the highest quality and best supported products in the industry ...

Data Center Project Manager

Milford, MA · On-site

$132K/yr

The data center market is booming. If you are a strong project manager, who is highly organized and wants to support an iconic brand of the highest quality and best supported products in the industry ...

The data center market is booming. If you are a strong project manager, who is highly organized and wants to support an iconic brand of the highest quality and best supported products in the industry ...

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Data Annotation Project Manager information

See Woonsocket, RI salary details

$16

$55

$76

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

As of Jul 8, 2026, the average hourly pay for data annotation project manager in Woonsocket, RI is $55.11, according to ZipRecruiter salary data. Most workers in this role earn between $47.69 and $64.52 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 Woonsocket, RI look for? The top searched job categories for Data Annotation Project Manager jobs in Woonsocket, RI are:
Infographic showing various Data Annotation Project Manager job openings in Woonsocket, RI as of July 2026, with employment types broken down into 2% Locum Tenens, 21% Full Time, 26% Part Time, 17% Contract, 33% Nights, and 1% Summer. Highlights an 34% Physical, and 66% Remote job distribution, with an average salary of $114,620 per year, or $55.1 per hour.

Technical Program Manager - Data Operations Lead

Zoox

Boston, MA

$140K - $181K/yr

Full-time

Re-posted 24 days ago


Job description

We are seeking an experienced and highly skilled operations leader to join the Perception Data and Labeling team. The team is responsible for training and evaluation data powering the perception (vision, lidar, and other modalities) ML models at Zoox. The candidate will work alongside ML engineers, product owners, data engineers, and external partners to scope and deliver high-quality training and evaluation data to help the Zoox Perception AI team meet critical deadlines.
In this role, you will:
  • Translate  and coalesce company-wide feature requirements into concrete and comprehensive data deliverables

  • Work collaboratively with AI teams, project management, and data annotation teams to manage the collection and labeling of training and evaluation data powering Zoox’s AI perception stack. 

  • Manage vendor allocation and budgeting in conjunction with the milestone and release timelines at Zoox. 

  • Define and maintain clear tracking of outcomes, risk, and data quality to ensure transparency and accountability.

Qualifications:
  • Experience in Technical Program Management or Operations Strategy: 5+ years of experience managing complex, cross-functional programs within the technology, autonomous driving, or AI/ML sectors.
  • Perception & Sensor Domain Knowledge: Strong understanding of the machine learning development lifecycle (MLDL) and familiarity with autonomous vehicle sensor modalities (LiDAR, Radar, Camera, Thermal) and their respective data annotation requirements.
  • Strategic Capacity Planning: Demonstrated ability to translate high-level product roadmaps and engineering requirements into granular operational execution plans. Experience managing resource allocation and throughput for large-scale workflows.
  • Vendor Ecosystem Understanding: Experience overseeing large-scale vendor operations or managed service providers, specifically regarding quality, latency, and volume targets.
  • Data Literacy: Proficiency with SQL or Python/Pandas to query databases, analyze throughput metrics, and generate reports without needing engineering support.

Base Salary Range

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.