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Data Manager – AI Development
Job in
Waukesha, Waukesha County, Wisconsin, 53188, USA
Listed on 2026-06-30
Listing for:
MS0073 GE Medical Systems, Ultrasound & Primary Care Diagnostics, LLC
Full Time
position Listed on 2026-06-30
Job specializations:
-
IT/Tech
Data Analyst
Job Description & How to Apply Below
Job Description Summary
The Data Manager – AI Development is a key role within the AI Development team responsible for planning, coordinating, tracking, and governing data used to develop AI enabled medical device features. This role works closely with AI/ML engineers to define data needs for AI features, coordinates with internal and external data collection teams/clinical team, oversees annotation activities, and ensures data readiness, traceability, and compliance throughout the AI development lifecycle.
The role is execution focused and coordination driven, ensuring that the right data is available, prepared, and documented at the right time to support AI feature development, evaluation, and regulatory readiness. Strong planning, communication, and organizational skills are essential for success in this role. Please note – this is a full time, onsite role located in Waukesha, WI.
Roles and Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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…
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