R&D Data Operations Lead — AI/ML & Clinical Data

DivIHN Integration Inc; Saint Paul (MN); On-site; USD 120,000 - 160,000; Full time

Job summary

DivIHN Integration Inc is seeking a Data Operations & R&D Data Management Manager to lead strategy, processes, and infrastructure enabling secure, scalable, and compliant data use across the product lifecycle.

The role oversees ingestion, transfer, cataloging, governance, quality, and accessibility while partnering with R&D engineers, clinical teams, IT/cloud, field personnel, physicians, external partners, and vendors to accelerate AI/ML model development and validation.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Health Informatics, Biomedical Engineering, or related discipline.
• 5+ years of experience in data management, data operations, data engineering, clinical data support, or related roles.
• Experience managing large and diverse datasets (clinical, imaging, mapping, time-series).
• Experience implementing data governance, metadata management, cataloging, and data quality processes.
• Experience coordinating data transfers and multi-stakeholder data initiatives.

Responsibilities

• Define and execute a roadmap for modernizing the EP R&D data ecosystem.
• Design and maintain data architecture, metadata standards, and common data models.
• Collaborate with IT/Cloud and engineering to enhance data infrastructure (Azure, Databricks).
• Oversee data ingestion, transfer, cataloging, governance, quality, and accessibility.
• Support data readiness for AI/ML development and regulatory submissions.

Skills

Data management

SQL

Python

Stakeholder management

Education

Bachelor's degree in Computer Science or related field

Tools

Azure

Databricks

Microsoft Purview

Job description

DivIHN Integration Inc is seeking a Data Operations & R&D Data Management Manager to lead strategy, processes, and infrastructure enabling secure, scalable, and compliant data use across the product lifecycle.

The role oversees ingestion, transfer, cataloging, governance, quality, and accessibility while partnering with R&D engineers, clinical teams, IT/cloud, field personnel, physicians, external partners, and vendors to accelerate AI/ML model development and validation.