AI-Powered Quality Ops Intern: Digital Analytics & BI
Philips Iberica SAU
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Benefits offered by this job
Job summary
Philips in Best, Netherlands, offers an Internship Assignment: AI-Powered Digital Daily Management for Quality Operations. You will support digital transformation of Quality Operations using Power BI, AI insights, and industrial data analytics to create a live daily management system with real‑time visibility of performance and bottlenecks.
The role emphasizes stakeholder collaboration, data‑driven decision making, and hands‑on experience in manufacturing analytics within a global health systems
Qualifications
- Must be pursuing a Bachelor or Master’s degree in a relevant field.
- Strong analytical and problem‑solving skills.
- Affinity with Power BI, AI, data analytics and process improvement.
- Able to translate data into actionable business insights and work independently.
Responsibilities
- Analyze existing operational data sources (SAP, SharePoint, quality reporting).
- Define KPIs including leading, lagging, quality and capacity indicators.
- Design and develop integrated Power BI dashboards for operational and management reporting.
- Create automated data flows and reporting structures to reduce manual KPI reporting efforts.
- Explore AI-assisted capabilities for trend monitoring, anomaly detection and insights.
- Visualize performance trends, workload distribution and bottlenecks.
- Translate operational needs into dashboard requirements and governance.
- Present findings to quality and operational leadership.
- Support continuous improvement and digital transformation initiatives.
Skills
Education
Tools
Job description
Philips in Best, Netherlands, offers an Internship Assignment: AI-Powered Digital Daily Management for Quality Operations. You will support digital transformation of Quality Operations using Power BI, AI insights, and industrial data analytics to create a live daily management system with real‑time visibility of performance and bottlenecks.
The role emphasizes stakeholder collaboration, data‑driven decision making, and hands‑on experience in manufacturing analytics within a global health systems
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