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AIS ISC Digital & AI Capabilities, Director

GE HealthCarePolandPosted 2 Nov 2026

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About this role

Job Description Summary We are seeking a Director to lead AIS ISC Digital & AI Capabilities, the AI, analytics, and data organization within the AIS Integrated Supply Chain. This leader-of-leaders role will oversee a team of approximately 18, including Digital Leads, Product Owners, and Data Governance specialists, supporting five core supply chain processes: Market to Order, Forecast to Plan, Source to Pay, Plan to Produce, and Produce to Deliver.

You will own the overall digital and analytics roadmap, ensuring governed KPI products, consistent data definitions, strong data governance, and trusted data foundations that enable business teams to self-serve analytics and AI solutions. A key priority will be leading Heartbeat, a unified SQDC analytics product designed to replace 500+ fragmented operational reports with a single, drillable source of truth integrated into daily management systems.

While your teams will build and own the governed KPI products, you will remain hands-on and technically credible, able to address complex cross-process analytics, executive requests, and emerging business needs. Job Description Responsibilities Lead the Organization Lead leaders. Select, develop, and grow four Digital Leads and a Data Governance Lead, with full accountability for the capability, performance, succession, and bench strength of the whole organization – Product Owners and Data Stewards included.

Hold the bar through your leads. Ensure the review standard for SQL, semantic models, and metric logic is real in every pillar – sampling the work often enough to know, without becoming the bottleneck. Build ownership, not dependency. Develop your Digital Leads’ direct relationships with their process owners and executives; measure your success by how well the organization operates without you. Own the Capabilities Portfolio – Analytics and AI One portfolio.

Own a single Capabilities roadmap spanning both the certified analytics and data products and the AI capabilities built on them – agentic AI, digital twin, predictive, simulation and optimization, and generative – built from the five pillar roadmaps, synchronized with segment and region priorities and agreed with the Global Process Owners – not a collection of separate backlogs. Make trade-offs explicit. Allocate capacity across pillars, arbitrate when demand exceeds it, and state clearly what the organization will not take on, and why.

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Protect the balance between running certified products, improving them, and building new capability. Deliver the Single Source of Truth Own the goal. Consolidate the Heartbeat Bowler KPIs (L0–L4) into one certified source of truth connected to daily management systems – the organization’s reason to exist. Lead Factory Heartbeat. Deliver the unified PSQDC analytics product – single drillable dashboard, integrated action tracking linked to problem solving, real-time data – and drive the disciplined retirement of the fragmented reporting it replaces.

Advance the Digital Framework maturity. Move each process from Organized to Digitized to Connected, with certified foundational data products as the gate – and report that maturity honestly to the business. Run the Hub, Enable the Spokes Own the fundamentals only the hub can own. Certified data products, one agreed definition per KPI, approved data connectors, platform standards, and certification – the non-negotiables that make everything built on top trustworthy.

Make the platform self-service. Publish certified products as documented, discoverable, consumable components – through Digital Compass and the data marketplace – so business users across segments, sites, and functions build their own analyses, reports, and AI use cases on trusted foundations, without a ticket queue. Set the certified / experimental boundary. Define what self-built, experimental work may touch versus what production and executive use requires – and provide the path for a spoke-built prototype to graduate into a hub-certified product.

Grow fluency in the spokes. Scale data and AI capability in the business through enablement, training, and communities of practice – platform adoption, not consumption of your team’s time, is the measure of success. Own Data Governance and KPI Arbitration Direct data governance. Through the Data Governance Lead and Data Stewards: documentation, validation, quality monitoring, correction of bad data, and publication of validated datasets to Digital Compass.

Arbitrate across pillars. Act as the escalation point for KPI definitions and cross-pillar decomposition – metrics such as On-Time Delivery and Past Due Backlog span the entire supply chain – and ensure every KPI has one agreed definition and a named accountable owner. Assign Data Owners deliberately. Per the organization’s design principles, place the Data Owner role with the most knowledgeable person within the Digital Lead and Product Owner group – ownership follows knowledge, not hierarchy.

Partner with CDAO on the enterprise governance, data platform, and data marketplace layer. Operate the Ways of Working Wire analytics into the operating model. Sub-process owners own standard work; KPI owners own standard bowlers; your organization certifies the numbers behind both – so that DMS runs on governed data, not local extracts. Run one front door. New process requirements raised in DMS flow through the single digital intake into the Capabilities roadmap – partner with the Transformation Programs pillar on intake, scoring, stage gates, value tracking, and adoption cadence.

Partner across the pillars. Work as one team with Digital Core for Business on process taxonomy (P1–P4), standard work, master data operationalization, and PLM/ERP alignment, so data definitions and process definitions never diverge. Show up in the business. Hold standing relationships with the five Global Process Owners and with segment and region leadership; represent Capabilities in executive operating mechanisms including the Monthly Operating Review – in the room with a point of view, not a report.

Deliver the AI Capabilities Portfolio AI capabilities are products of this organization. The agentic AI factory, digital twin, forecasting, simulation and optimization, and generative capabilities are part of the Capabilities portfolio you own – managed with the same roadmap, certification, and ownership discipline as the data products, not as side projects. Sequence AI behind certified data. Own the foundational datasets – documented, validated, published – on which AI use cases are built, and hold that line when delivery pressure arrives.

Scale the AI factory hub-and-spoke. The hub certifies data connectors, agent standards, and ownership under one interface; the spokes build agents and use cases on them – govern grassroots AI energy without extinguishing it. Advance the capability curve. From descriptive analytics through predictive, simulation and optimization, digital twin, and generative and agentic capabilities – toward a connected, intelligent enterprise in which people always own the decisions.

Embed AI-enabled ways of working across your own organization first, and scale what works to the spokes. Required Qualifications Bachelor’s degree in Supply Chain Management, Data Science, Information Systems, Business Analytics, Engineering, or a related quantitative field. Strong professional experience in analytics, data, or supply chain, including 5+ years leading analytics or data teams and experience leading leaders (managing managers or team leads).

Hands-on SQL and modern BI – able to pull and transform data and build an analysis independently, and to review and challenge query logic, semantic models, and metric calculations across multiple teams. Track record of owning an enterprise analytics portfolio and roadmap end to end, agreed with senior business stakeholders across functional boundaries. Experience establishing KPI hierarchies, definitions, and ownership models across functions, and arbitrating metric disputes between domains.

Working knowledge of data governance – definitions, data quality management, stewardship, and metadata practices – including operationalizing governance through dedicated roles. Experience partnering with senior operational leadership in executive operating mechanisms, and influencing without authority in a global, matrixed organization. Preferred Qualifications Deep domain expertise in two or more supply chain process areas – logistics, manufacturing, sourcing, planning, or order execution.

Applied experience with AI and machine learning in an enterprise setting, including LLM / GenAI and agentic tooling, and responsible AI practices. Experience with cloud data platforms such as Microsoft Fabric, AWS Redshift, Snowflake, or Azure Synapse, and with enterprise systems such as Kinaxis, SAP, or Oracle Fusion. Experience consolidating a fragmented reporting landscape into certified data products, including the retirement of legacy reporting.

Experience operating a hub-and-spoke or federated analytics model – building self-service data platforms, enablement programs, or communities of practice that scale capability beyond the central team. Lean / Six Sigma or equivalent process improvement background; familiarity with tiered daily management systems (DMS). What We Look For Builds leaders, not dependencies – measures success by how well the organization runs without them, and develops Digital Leads who could hold this seat one day.

Technically credible – still writes SQL and builds their own analysis, which is what makes their review of the organization’s work land. Decisive on trade-offs – says what the organization will not do, and why, to the business – one roadmap, not five backlogs. Protects foundations – holds the line on certification, documentation, and stewardship when delivery pressure arrives; sequences AI behind trusted data. Platform builder, not report factory – measures success by what the business builds self-serv

Source listing: himalayas_poland_p2

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