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Data Annotation Manager Jobs in Milwaukee, WI (NOW HIRING)

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Data Annotation Manager information

See Milwaukee, WI salary details

$30.5K

$95.7K

$169.5K

How much do data annotation manager jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data annotation manager in Milwaukee, WI is $95,711.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,000.00 and $123,600.00 per year, depending on experience, location, and employer.

What is the salary of data annotation manager?

The salary of a data annotation manager typically ranges from $60,000 to $120,000 annually, depending on experience, location, and company size. Senior roles or those in high-cost areas may offer higher compensation, and familiarity with annotation tools and team management can influence pay levels.

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 are met, often requiring familiarity with annotation tools and project management skills.

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 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.

Does data annotation actually pay well?

Data annotation managers typically earn competitive salaries that reflect their experience and responsibilities, often ranging from entry-level to senior roles. Compensation can vary based on industry, location, and company size, with specialized skills in tools like labeling platforms and quality control often leading to higher pay.

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

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.

How hard is it to get hired by data annotation?

Getting hired as a data annotation manager typically requires relevant experience in data labeling, familiarity with annotation tools, and strong organizational skills. The hiring process often involves reviewing previous work, technical assessments, and demonstrating attention to detail, with opportunities available in companies that outsource data labeling tasks.

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 Milwaukee, WI? The most popular types of Data Annotation jobs in Milwaukee, WI are:
What are popular job titles related to Data Annotation Manager jobs in Milwaukee, WI? For Data Annotation Manager jobs in Milwaukee, WI, the most frequently searched job titles are:
What job categories do people searching Data Annotation Manager jobs in Milwaukee, WI look for? The top searched job categories for Data Annotation Manager jobs in Milwaukee, WI are:
Data Annotation Manager jobs near you
Data Manager - AI Development

Data Manager - AI Development

GE HealthCare

Waukesha, WI • On-site

Full-time

Re-posted 3 days ago

GE HealthCare rating

8.3

Company rating: 8.3 out of 10

Based on 135 frontline employees who took The Breakroom Quiz

92nd of 430 rated machine equipment manufacturers

Job description

Job Summary:
GE HealthCare is a leader in healthcare innovation, and they are seeking a Data Manager for their AI Development team. This role is responsible for planning, coordinating, tracking, and governing data used to develop AI-enabled medical device features, working closely with AI/ML engineers and various stakeholders to ensure data readiness and compliance throughout the development lifecycle.
Responsibilities:
• AI Data Planning & Requirements
• Partner with AI/ML engineers and technical leads to define data requirements for AI features, including dataset scope, diversity, and usage intent.
• Translate feature and model needs into clear data requirements that guide collection, annotation, and preparation activities.
• Support creation and maintenance of AI data planning artifacts aligned with internal Quality Management System (QMS) requirements.
• Data Collection Coordination
• Coordinate with centralized and distributed data collection teams to support AI development needs.
• Track data sourcing activities across multiple programs and stakeholders.
• Maintain data collection dashboards that provide visibility into status, coverage, risks, and gaps.
• Track data collection and annotation budget.
• Annotation & Labeling Oversight
• Coordinate data annotation activities with internal teams and external vendors.
• Track annotation progress, throughput, and quality metrics.
• Maintain annotation dashboards to ensure timely delivery aligned with AI development milestones.
• Data Governance & Compliance Support
• Support execution of AI data management practices including:
• Data control planning
• Data segregation between training, holdout, and testing datasets
• Data preparation and inclusion criteria
• Data traceability and usage documentation
• Ensure datasets are properly documented and traceable to their original sources to support audits and regulatory submissions.
• Act as a point of coordination to ensure data activities align with applicable QMS work instructions for AI development.
• Program Tracking & Communication
• Serve as the central coordination point for AI data activities across engineering, data operations, and program teams.
• Proactively communicate status, risks, and dependencies to stakeholders.
• Support planning reviews, design reviews, and readiness discussions with accurate data status reporting.
Qualifications:
Required:
• Bachelor’s degree in Engineering, Computer Science, Data Science, Biomedical Engineering, or a related technical discipline with 4 years of experience.
• Experience in data management, data operations, or program coordination roles supporting technical or engineering teams.
• Demonstrated ability to plan, track, and coordinate complex workflows across multiple stakeholders.
• Strong written and verbal communication skills, with the ability to translate technical needs into actionable plans.
• Experience creating and maintaining dashboards (eg. PowerBI, excel, smartsheet) trackers, or reports for operational visibility.
• Familiarity with structured data workflows(eg. SQL), including data collection, annotation, and dataset organization(eg. Python).
• Ability to work effectively in cross‑functional teams within a regulated or quality‑driven environment.
Preferred:
• Experience supporting AI / machine learning development teams, particularly in healthcare or medical devices.
• Familiarity with AI data lifecycle concepts, including training, validation, and testing datasets.
• Knowledge of medical imaging data formats and annotation tools (e.g., V7).
• Exposure to regulated development environments (medical devices, healthcare software, or similar).
• Understanding of data governance concepts such as data traceability, segregation, and controlled usage.
• Experience coordinating external vendors or annotation partners.
• Comfort working with ambiguity and evolving requirements in early‑stage AI feature development.
• Experience with Microsoft Forms
Company:
GE Healthcare is a healthcare company with intelligent devices, data analytics, applications, and services-supported intelligence platforms. Founded in 1892, the company is headquartered in Helsinki, FIN, with a team of 10001+ employees. The company is currently Late Stage.

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