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Internship Data Annotation Analyst Jobs in Wisconsin

... annotation, and preparation activities. • Support creation and maintenance of AI data planning ... GE Healthcare is a healthcare company with intelligent devices, data analytics, applications, and ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... reviewing applications, analyzing resumes, or assessing responses and identifying potential ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... reviewing applications, analyzing resumes, or assessing responses and identifying potential ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... reviewing applications, analyzing resumes, or assessing responses and identifying potential ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... reviewing applications, analyzing resumes, or assessing responses and identifying potential ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... reviewing applications, analyzing resumes, or assessing responses and identifying potential ...

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate ... reviewing applications, analyzing resumes, or assessing responses and identifying potential ...

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Internship Data Annotation Analyst information

How hard is it to get hired by data annotation?

Getting hired as a data annotation analyst generally requires attention to detail, basic computer skills, and familiarity with annotation tools or platforms. Many positions are entry-level and may not require extensive experience, making the role accessible to beginners, though some jobs may prefer knowledge of specific data types or industry standards.

What is a data annotation internship?

A data annotation internship is a temporary position where interns assist in labeling and categorizing data, such as images, text, or videos, to help train machine learning models. Interns typically use annotation tools and develop skills in data quality and accuracy under supervision. This role provides practical experience in data preparation and AI development processes.

What does a data annotation analyst do?

A data annotation analyst is responsible for labeling and categorizing data, such as images, text, or videos, to prepare it for machine learning models. They use tools and follow guidelines to ensure data accuracy and consistency, which is essential for training effective AI systems.

What is the difference between Internship Data Annotation Analyst vs Data Labeling Specialist?

AspectInternship Data Annotation AnalystData Labeling Specialist
CredentialsTypically pursuing or recent graduate in related fieldRelevant certifications or experience preferred
Work EnvironmentInternship setting, often in tech or AI companiesFull-time or freelance roles in data annotation companies
Industry UsageCommon in AI, machine learning, and tech industriesUsed across AI, autonomous vehicles, and healthcare sectors

The Internship Data Annotation Analyst is usually an entry-level role for students or recent graduates gaining experience in data annotation. In contrast, Data Labeling Specialists are more experienced professionals focused on accurately labeling data for AI models. Both roles are essential in AI development, but the internship provides learning opportunities, while the specialist role involves more independent work and expertise.

Does data annotation actually pay?

Data annotation analysts are typically paid for their work, with compensation varying based on the project, platform, or employer. Many companies offer hourly rates or per-task payments, and some roles may require specific skills or tools. Payment is generally reliable for those working in paid annotation jobs, especially with established platforms or companies.
What are the most commonly searched types of Data Annotation Analyst jobs in Wisconsin? The most popular types of Data Annotation Analyst jobs in Wisconsin are:
Data Manager - AI Development

Data Manager - AI Development

GE HealthCare

Waukesha, WI • On-site

Full-time

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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