Supply Chain Planning and Operations; SCPO) AI Lead

The Supply Chain Planning Operations (SCPO) organization is seeking an AI Software Architect to serve as the SCPO AI Lead. This is a founding technical leadership role embedded within supply planning operations. The role will define and execute the AI strategy for connecting planning and execution data, detecting emerging supply, flagging demand and product risks, and enabling planners and leaders to act on trusted, explainable insights.

The SCPO AI Lead will architect and build the intelligent stitching and alerting layer across Plan of Record (POR), Long Range Planning (LRP), Sales and Operations Planning (SnOP), Enterprise Resource Planning (ERP), and other supply chain related data. The role will convert fragmented data from multiple sources and manual reconciliation into production capabilities that continuously monitor supply execution, identify likely failures, explain the drivers, and guide timely intervention.

The successful candidate will deliver production solutions used in operational decisions, work directly with planners, and establish the technical foundation and engineering practices for SCPO. Please note that this is not a research-only or advisory role.

• Define the SCPO AI and data intelligence strategy, architecture, roadmap, and standards in alignment with supply-chain priorities and measurable business outcomes.
• Architect and operationalize a trusted data stitching layer across POR, LRP, SnOP, ERP, and other planning data sources, including lineage, controls, reconciliation, and data-quality monitoring and governance.
• Develop production machine-learning and AI capabilities for time-series analysis, anomaly detection, root-cause identification, supply-risk sensing, and early warning of execution failures.
• Integrate AI outputs with enterprise planning systems and operational workflows using reliable APIs, data pipelines, monitoring, access controls, and override mechanisms.
• Build agentic workflows that automate recurring analyses, evidence gathering, exception triage, and narrative generation while maintaining human-in-the-loop controls, auditability, and appropriate governance.
• Develop scenario and sensitivity capabilities that evaluate supply disruptions, demand changes, yield shifts, capacity reallocations, material constraints, and other operational decisions across planning horizons.
• Create explainable alerts and decision-support experiences that show what changed, why it matters, the level of confidence, and the recommended next action.
• Map the planning-system ecosystem, data flows, manual intervention points, process breakpoints, dependencies, and failure modes; prioritize automation and guardrails based on business risk and value.
• Partner with supply planning, demand planning, manufacturing, foundry, procurement, finance, data engineering, information technology, and business teams to translate operational problems into scalable technical solutions.
• Establish MLOps and software-engineering practices for model versioning, testing, deployment, observability, retraining, documentation, security, and responsible AI governance.
• Communicate technical findings, model limitations, risk signals, architecture decisions, and business recommendations clearly to planners, technical partners, senior managers, and executives.
• Operate with intellectual rigor in ambiguous environments where data, systems, and requirements are incomplete or evolving.
• Commitment to explainability, responsible AI, human oversight, security, and governance for decisions with material financial or operational consequences.

Initial success measures include:
Within 90 days: define the target stitching architecture, data ownership model, prioritized use-case roadmap, and deploy an initial live reconciliation capability. Within 6 months: deploy an explainable alert that detects a material supply-execution risk early enough to support intervention. Within 12 months: demonstrate sustained planner adoption, measurable reduction in manual reconciliation, and operational decisions informed by trusted alerts and scenarios.

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Industrial Engineering, Operations Research, Applied Mathematics, or a related quantitative field.
• 8+ years of experience in machine-learning engineering, AI engineering, data science, data engineering, optimization, or related technical roles, including leadership of complex production deployments.
• Architecting,…