Project Manager - Data Analytics (Supply Chain)

Siemens Healthineers

8d•Hybrid

About The Position

Join us in pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. Our inspiring and caring environment forms a global community that celebrates diversity and individuality. We encourage you to step beyond your comfort zone, offering resources and flexibility to foster your professional and personal growth, all while valuing your unique contributions. Varian Medical Systems, a Siemens Healthineers company is seeking a highly analytical, technically skilled, and results-oriented Project Manager with a Data Analytics emphasis to lead strategic data initiatives within Varian’s Supply Chain Plan & Materials organization. In this role, you will drive high-impact analytics, digital transformation, and process-improvement projects that enhance supply chain visibility, planning accuracy, operational efficiency, and overall performance. You will partner closely with cross-functional teams to convert complex data into actionable insights, streamline core planning and materials workflows, and embed a culture of data-driven decision-making across SCM Plan & Materials. Preference is for a candidate who can be onsite 2 days/week at a Varian location

Requirements

Bachelors degree in: Computer Science, Supply Chain, Data Analytics, or related field and 3+ years of experience OR Masters degree and 1+ years of experience
Proven experience in project management and data analytics within a supply chain, operations, or manufacturing environment.
Advanced SAP expertise in planning and purchasing processes, including MRP, BOM management, source list/Info Records, and production planning (MM, PP, related modules).
Track record of leading analytics and process optimization projects from concept through execution, delivering measurable improvements in performance.
Strong analytical and problem-solving capabilities, with the ability to translate data into actionable insights and process enhancements.
Proficiency with data visualization and analytics tools (Power BI, Qlik Sense) and data analysis languages/tools (SQL, VBA, Python, or R).
High attention to detail and strong commitment to data quality, accuracy, and governance.
Exceptional communication, collaboration, and stakeholder management skills, with demonstrated ability to influence across all organizational levels.

Nice To Haves

Experience in supply chain analytics, including forecasting, inventory optimization, and supplier performance measurement.
Experience with SAP analytics and data integration technologies (BW, HANA, or Snowflake-based data platforms).
Knowledge of Lean, Six Sigma, Agile, or other continuous improvement/digital transformation methodologies.
Experience developing and deploying machine learning or AI models to support predictive and prescriptive analytics.

Responsibilities

Lead cross-functional analytics and process improvement initiatives to optimize planning, purchasing, material flow, and overall supply chain performance.
Analyze complex supply chain challenges using SAP data (MM, PP, MRP) and other enterprise systems to identify operational gaps, risks, and opportunities.
Design, automate, and optimize data pipelines and reporting workflows to improve data accuracy, consistency, and timeliness across planning and procurement activities.
Develop dynamic dashboards and visualizations (Power BI, Qlik Sense, etc.) to communicate performance trends, forecast accuracy, inventory health, material availability, and supplier performance.
Collaborate with Material Master Data, Supply Chain, Operations, Procurement, Finance, and IT partners to ensure analytics initiatives align with business objectives and digital transformation strategies.
Establish and maintain strong data governance practices across SAP and analytics platforms to support reliable, real-time decision-making.
Champion analytics adoption and data literacy, enabling planners, buyers, and leaders to leverage insights in daily operations and strategic planning.
Drive continuous improvement, documenting processes, standardizing data workflows, and implementing scalable solutions that enhance supply chain visibility and responsiveness.