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Data Annotation For Ai Jobs in Wisconsin (NOW HIRING)

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... Potential for ongoing project participation We may use artificial intelligence (AI) tools to ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... Potential for ongoing project participation We may use artificial intelligence (AI) tools to ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... Potential for ongoing project participation We may use artificial intelligence (AI) tools to ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... Potential for ongoing project participation We may use artificial intelligence (AI) tools to ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... Potential for ongoing project participation We may use artificial intelligence (AI) tools to ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... Potential for ongoing project participation We may use artificial intelligence (AI) tools to ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

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Data Annotation For Ai information

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

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

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.
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Infographic showing various Data Annotation For Ai job openings in Wisconsin as of July 2026, with employment types broken down into 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.
Data Manager - AI Development

Data Manager - AI Development

GE HealthCare

Waukesha, WI • On-site

Full-time

Posted 15 days ago


GE HealthCare rating

8.4

Company rating: 8.4 out of 10

Based on 137 frontline employees who took The Breakroom Quiz

99th of 485 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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