GSEC Process, Tooling & Data Analytics Specialist (m/f/d)

Analyze, define and continuously improve global GSEC and support-related processes, workflows and interfaces. Develop pragmatic process standards, templates, working instructions and governance material that are easy to adopt globally. Own and improve tooling concepts for Jira, Confluence, dashboards, reporting solutions and other collaboration platforms. Build and maintain KPI structures, dashboards and recurring reports that convert operational data into actionable management insights. Perform data analysis to identify trends, bottlenecks, recurring failure patterns, process gaps and improvement opportunities. Support automation, digitalization and AI-enablement initiatives that increase transparency, knowledge reuse and operational efficiency. Translate stakeholder needs into tool requirements, user stories, improvement backlogs and implementation proposals. Represent Product Support process and tooling requirements in cross-functional project discussions, especially during NPI planning, pilot phases and readiness reviews. Facilitate cross-functional workshops, retrospectives and improvement activities with Product Support, Field Service, R&D, Quality and Manufacturing. Use support case and technical escalation context to ensure that processes and tools solve real operational problems without making this a pure technical support role. Support the new product introduction phase by participating in cross-functional meetings and ensuring that Product Support requirements, serviceability needs, documentation needs and operational readiness aspects are considered early enough. Contribute to knowledge-management concepts that make lessons learned, troubleshooting information and best practices easier to find and reuse. Data Analytics Focus Create meaningful KPI dashboards and reports for backlog, cycle time, response quality, resolution trends, NTF/DFS-related patterns and improvement follow-up. Define data views that help teams distinguish symptoms from root causes and prioritize improvement actions based on evidence. Improve data quality by clarifying definitions, ownership, input discipline and reporting routines. Use analytics to support management reviews, process retrospectives, project follow-up and cross-functional escalation discussions. Use data analytics to support NPI readiness decisions, including early visibility of known issues, support risks, documentation gaps and follow-up actions. Identify opportunities for automation, AI-assisted knowledge retrieval and predictive indicators for support workload or quality risks.

Degree in industrial engineering, business administration, computer science, information systems, data analytics or a comparable qualification. Experience in process management, business analysis, operations excellence, service management or project management. Strong analytical mindset with the ability to structure complex information, identify improvement levers and communicate insights clearly. Hands-on experience with Jira, Confluence or comparable workflow and knowledge-management platforms. Solid understanding of KPI definition, data quality, reporting logic and dashboard-driven management routines. Ability to work with operational data sets and derive practical recommendations for management and teams. Excellent communication, moderation and stakeholder-management skills in an international environment. Good technical understanding of support or engineering workflows; deep product expert knowledge is helpful but not mandatory. Experience participating in cross-functional engineering, NPI, readiness or launch-related meetings and translating support requirements into clear actions. Fluent English skills, both written and spoken; German or Japanese language skills are an advantage. Experience with Power BI, Power Automate, advanced Excel, SQL, Python or comparable analytics and automation tools. Knowledge of Lean, Six Sigma, BPMN, process mining, ITIL or service management frameworks. Experience in a global service, product support, quality or engineering organization. Familiarity with semiconductor test systems or other complex high-tech capital equipment. Experience with AI-supported process optimization, knowledge management, chatbot enablement or digital assistant concepts.