This role pays less than
85%
of similar roles. Most pay
$175,125–$246,150
— the shaded band above.
At the midpoint, this role pays about
$153k
versus about
$211k
for comparable roles.
Based on 239 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
69 open roles
on FindRole.
Listed pay typically runs
$122,000–$204,000
across 67 roles with salary data.
As a Technology Manager for Quality Systems and AI Lead at DePuy Synthes, you will play a pivotal role in shaping the digital quality landscape within Supply Chain Technology. Your responsibilities include leading the design and continuous improvement of quality systems to ensure compliance, reliability, and scalability while embedding AI-driven innovation. You will oversee system validation, testing, and continuous improvement initiatives, integrate advanced analytics into quality systems for enhanced monitoring and predictive insights, and establish performance metrics to drive system optimization. Collaborating with IT, Supply Chain, Quality, and Regulatory teams, you will manage a team, provide coaching, and foster a culture of innovation and accountability. The ideal candidate has over 10 years of experience in IT or digital quality management, expertise in regulatory compliance and risk management, and proficiency in AI/ML applications. Familiarity with enterprise platforms like ERP and MES is preferred, as well as knowledge of Lean Six Sigma methodologies.
AI
Change Management
Project Management
Communication Skills
Generative AI Tools
Microsoft SharePoint
Microsoft Teams
Curriculum Design
Training Delivery
Storytelling
Video Production
CI/CD
Analytics
Python
SQL
PowerPoint
Confluence
JIRA
Trello
Zoom
Slack
AI
ML
Datacenter
Cloud
Product Management
Systems Thinking
Competitive Analysis
GenAI
ML Accelerators
Hardware
Software
Infrastructure
Performance Analysis
System Layers
End-to-End System Behavior
Problem Solving
Technical Analysis
Customer Workloads
Product Roadmap
Engineering Priorities