Your Main Responsibilities
- Support both the definition and execution of the digital transformation strategy and roadmap
- Foster innovation and identify opportunities for new digital solutions
- Ensure compliance with data governance, security, and internal control standards
- Assess new digital initiatives for feasibility, business value, and impact
- Promote a digital mindset and drive change management and technology adoption
- Coordinate AI-related initiatives and oversee the AI Specialist
- Monitor and report on key KPIs (ROI, digitalization level, efficiency gains, tech adoption, budget adherence, customer satisfaction)
- Lead and coach Digital Transformation Business Partners across the organization
- Manage vendors and budgets in collaboration with Procurement and the PMO
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YOUR RESPONSIBILITIES
- Design and implement agentic workflows, tool/skill integrations and orchestration logic for operational use cases
- Develop safe and reliable execution patterns for agentic systems, including guardrails, fallbacks, retries and auditability
- Integrate agentic capabilities with internal systems, such as case/ticket workflows, automation engines and digital product components
- Contribute to the design of structured context models, typed tool interfaces and decision flows
- Evaluate and improve agent performance through testing, evaluations, feedback loops and production monitoring
- Support internal/admin tools for the configuration, observability and review of agent behaviour
- Collaborate closely with product managers, engineers and domain experts to translate process knowledge into reliable agentic software
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Key Responsibilities
- Align R&D digital initiatives with strategic priorities.
- Identify opportunities to digitalize and streamline R&D processes.
- Manage digital transformation projects from planning to delivery (scope, time, budget).
- Ensure a key user approach for relevant R&D applications is established, including UAT coordination and first-level support.
- Ensure effective communication, stakeholder alignment, and change adoption.
- Support Data Governance (integrity, traceability, access control, audit readiness).
- Promote compliance, quality, safety, and CSR guidelines.
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YOUR RESPONSIBILITIES
- Design and implement backend services for an incident‑automation platform, including telemetry processing, orchestration, workflow execution, case/ticket integrations, and platform APIs
- Evolve the current architecture from tightly coupled workflows and database‑driven logic to a modular, event‑driven, and scalable backend platform
- Build and maintain reliable integrations with charger telemetry, ticketing/case systems, service workflows, and customer‑facing product components
- Improve workflow reliability, idempotency, retry mechanisms, state management, error handling, and auditability across automated playbooks
- Collaborate with product, service, R&D, and data engineering teams to translate operational playbooks into maintainable backend capabilities
- Contribute to technical decisions on rules/policy handling, workflow orchestration, context modeling, observability, and system scalability
- Support the implementation of secure, governed, and monitorable AI/agent‑based backend capabilities where appropriate
- Help define engineering standards, testing strategies, deployment patterns, and the long‑term backend architecture
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Positionsübersicht
- Entwurf und Verwaltung von Datenspeichersystemen für große Datensätze (Multi-TB-Bilddaten, 3D-Assets, Trainingsdaten)
- Entwicklung effizienter Datenzugriffsmuster und Bewegungsstrategien für verteiltes Training und Experimentieren
- Implementierung der Versionierung von Datensätzen und Verfolgung der Abstammung für die Reproduzierbarkeit
- Einrichtung und Pflege der Infrastruktur für Experimentverfolgung und Modellregistrierung (MLflow, Weights & Biases)
- Aufbau von ML-Pipelines für Datenvorverarbeitung, Training, Validierung und Modellregistrierung (Kubeflow, Airflow, Prefect)
- Unterstützung verteilter Trainingsworkflows über Multi-GPU-Cluster (PyTorch Distributed, Horovod, Ray)
- Profilierung und Optimierung von Trainingspipelines: Engpässe beim Laden von Daten, Batch-Sizing, GPU-Speicherauslastung
- Sicherstellung der Reproduzierbarkeit von Experimenten: Umgebungspinning, Datenversionierung, Artefaktmanagement
- Verwaltung der Speicherung und Verteilung von Artefakten (Docker-Registrierungen, Modell-Registrierungen, Paket-Repositories)
- Entwicklung von Werkzeugen zur Verbesserung der Entwicklerproduktivität für ML-Workflows
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Dr. Schär is a global community of innovative and collaborative people, free to think outside the box and to unlock their full potential in terms of creativity, capability, and courage.
- Define and enforce group‑wide IT policies, architectures, and standards.
- Establish a unified governance model for transparency and effective decision‑making.
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YOUR RESPONSIBILITIES
- Develop, maintain and optimize Business Intelligence and reporting solutions to enhance financial and business performance tracking
- Design and manage efficient data warehouses for streamlined data storage and retrieval, leveraging AI where applicable
- Implement data-driven controlling strategies, ensuring accuracy, transparency and reliability in financial assessments
- Ensure data integrity and consistency across multiple systems
- Utilize advanced analytics and visualization tools (e.g., Power BI, Tableau, SQL, Python) to create insightful reports and dashboards
- Collaborate with cross-functional teams to improve data collection processes and drive continuous improvement
- Support the automation of controlling workflows by identifying opportunities for increased efficiency
- Build and simulate financial models, providing analysis, budgeting, ROI calculations and other insights to support business planning
- Define and implement standardized processes and reporting structures for budgeting and financial reporting, with a strong focus on process improvement and simplification
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