R&D Data Management Manager

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Title:

R&D Data Management Manager AI/ML & Clinical Data

Location:

Onsite l, MN Duration: 12 Months

Hours:

9 AM - 5 PM Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered Position Summary Client's Electrophysiology (EP) R&D organization generates an expanding volume of clinical, imaging, mapping, and outcomes data that serves as the foundation for next-generation AI/ML solutions. To accelerate innovation, we are seeking a Data Operations & R&D Data Management Manager to lead the strategy, processes, and infrastructure that enable secure, scalable, and compliant use of data across the product development lifecycle.

This role combines data platform leadership with operational execution. The successful candidate will oversee data ingestion, transfer, cataloging, governance, standardization, quality, and accessibility while partnering closely with R&D engineers, Clinical teams, IT/Cloud organizations, field personnel, physicians, external partners, and vendors. The role will ensure that clinical and field-derived datasets are readily available, trustworthy, compliant, and optimized for AI/ML development, validation, and regulatory submission activities.

Key Responsibilities Data Strategy, Platform, and Infrastructure Define and execute a roadmap for modernizing the EP R&D data ecosystem through scalable, searchable, and automated data management capabilities. Design and maintain data architecture, metadata standards, clinical dictionaries, and common data models spanning cardiac mapping, imaging, and clinical datasets. Partner with IT, Cloud, and engineering teams to implement and enhance data infrastructure leveraging Azure, Databricks, and related enterprise platforms.

Evaluate and implement tools supporting data cataloging, governance, version control, lineage, labeling, storage, and AI/ML workflows. Ensure data systems effectively support both Windows and Linux-based development environments. Data Operations and Dataset Readiness Coordinate end-to-end acquisition, transfer, ingestion, validation, and management of clinical and field-derived datasets. Establish processes to ensure completeness, integrity, traceability, and accessibility of data throughout its lifecycle. Partner with physicians, clinical sites, field teams, and data owners to support strategic data collection and partnership initiatives.

Manage data transfer activities, issue resolution, status tracking, and stakeholder communications to ensure timely delivery of datasets. Collaborate with engineering and AI/ML teams to address data quality issues, missing metadata, ingestion failures, and standardization gaps. Support dataset versioning, lineage tracking, reproducibility, and audit readiness for AI/ML model development and validation. AI/ML Data Enablement Work directly with AI/ML and R&D engineering teams to understand data requirements and accelerate model development efforts.

Build and improve processes that enable efficient discovery, access, preparation, and validation of datasets. Oversee labeling and annotation programs, including external…