GSEC Process, Tooling & Data Analytics Specialist; m/f/d
in
71032, Böblingen, Baden-Württemberg, Deutschland
Verfasst am 2026-09-30
Unternehmen:
RxREVU, Inc.
Vollzeit
position Verfasst am 2026-09-30
Berufliche Spezialisierung:
-
IT/Informationstechnik
IT Business Analyst, Wirtschaftsinformatik, Änderungsmanagement, AI Künstliche Intelligenz -
Wirtschaft
Wirtschaftsinformatik, Änderungsmanagement, AI Künstliche Intelligenz
Stellenbeschreibung
Location: Böblingen
- 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.
- 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.
- 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…
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