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Data Annotation Program Manager Jobs in Wisconsin

As the AI Program Manager, you will build and run a program of AI initiatives that create ... Work with Business and IT to develop data and IT infrastructure and tools to support AI program ...

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Data Annotation Program Manager information

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

To thrive as a Data Annotation Program Manager, you need expertise in project management, data quality assessment, and a solid understanding of machine learning or data annotation processes, typically supported by a relevant degree. Familiarity with annotation platforms, workflow management tools, and data labeling software is essential, along with knowledge of quality assurance frameworks. Strong leadership, problem-solving abilities, and effective communication are crucial soft skills that help manage diverse teams and ensure stakeholder alignment. These skills are important to maintain high data quality, meet project deadlines, and drive successful AI model training initiatives.

How does a Data Annotation Program Manager coordinate with cross-functional teams to ensure project success?

A Data Annotation Program Manager regularly collaborates with engineering, data science, and quality assurance teams to align annotation guidelines, project timelines, and quality standards. They often facilitate meetings to clarify requirements, resolve ambiguities in data labeling, and provide feedback on annotation accuracy. This role serves as a bridge between technical teams and annotation staff, ensuring open communication and timely resolution of challenges, which is critical for delivering high-quality datasets essential for machine learning and AI projects.

What are Data Annotation Program Managers?

Data Annotation Program Managers are professionals who oversee and coordinate data labeling projects, ensuring that data used for machine learning and artificial intelligence is accurately tagged and prepared. They manage teams of annotators, set project guidelines, monitor quality, and ensure deadlines are met. Their role is crucial for building high-quality datasets that enable reliable AI model training. Program Managers often collaborate with data scientists, engineers, and stakeholders to define requirements and improve annotation processes.
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Data Manager - AI Development

Data Manager - AI Development

GE HealthCare

Waukesha, WI • On-site

Full-time

Re-posted 5 days ago


GE HealthCare rating

8.3

Company rating: 8.3 out of 10

Based on 136 frontline employees who took The Breakroom Quiz

80th 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:
Every day millions of people feel the impact of our intelligent devices, advanced analytics and artificial intelligence. Founded in 1989, the company is headquartered in Oslo, NOR, with a team of 10001+ employees. The company is currently Late Stage.

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