Supply Chain Planning and Operations – AI Lead

Intel Corporation

. Define the SCPO AI and data intelligence strategy, architecture, roadmap, and standards .

Posted 11/1/2026full-timeUnited StatesSenior💰 $170,500 - $315,490 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining AI and data intelligence strategies, architecting machine-learning solutions, and integrating AI outputs with enterprise systems. Proficient in data governance, scenario modeling, and leading cross-functional teams to drive operational excellence.

Highest-signal resume keywords

Machine-Learning EngineeringData EngineeringPython ProgrammingSQL SkillsMLOps Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine-Learning SolutionsData Pipeline DesignAPIs IntegrationData Quality MonitoringTime-Series AnalysisAnomaly DetectionRoot-Cause IdentificationScenario ModelingData GovernanceOperational Decision-Making

Soft Skills

Advanced Communication SkillsCross-Functional LeadershipTolerance for Responsible Experimentation

Tools & Technologies

SAPKinaxis Rapid ResponseOracleCloud Data PlatformsDistributed Data ProcessingManufacturing Execution Systems

Certifications & Qualifications

APICS CPIMAPICS CSCP

Industry Keywords

Supply PlanningDemand PlanningIntegrated Business PlanningCapacity PlanningInventory OptimizationProcurementOrder ManagementSemiconductorsElectronicsHigh-Volume Manufacturing

Tech Stack

Tools & technologies

CloudERPOraclePythonSQL

About the role

Key responsibilities & impact

Define the SCPO AI and data intelligence strategy, architecture, roadmap, and standards
Architect and operationalize a trusted data stitching layer across POR, LRP, SnOP, ERP, and other planning data sources
Establish lineage, controls, reconciliation, 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 APIs, data pipelines, monitoring, access controls, and override mechanisms
Build agentic workflows for recurring analyses, evidence gathering, exception triage, and narrative generation with human-in-the-loop controls and auditability
Develop scenario and sensitivity capabilities for supply disruptions, demand changes, yield shifts, capacity reallocations, material constraints, and other operational decisions
Create explainable alerts and decision-support experiences showing changes, impact, confidence, and recommended next actions
Map planning-system ecosystems, 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
Establish MLOps and software-engineering practices for versioning, testing, deployment, observability, retraining, documentation, security, and responsible AI governance
Communicate technical findings, model limitations, risk signals, architecture decisions, and business recommendations to planners, technical partners, senior managers, and executives
Deliver target architecture, data ownership model, roadmap, and live reconciliation capability within 90 days
Deploy an explainable alert for material supply-execution risk within 6 months
Demonstrate planner adoption, reduced manual reconciliation, and trusted alert- and scenario-informed decisions within 12 months

Requirements

What you’ll need

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
Experience architecting, deploying, and operating machine-learning solutions in production on complex enterprise data
Programming experience in Python
Strong SQL skills
Practical experience in modern machine-learning, data-processing, and software-engineering frameworks
Experience designing data pipelines, APIs, and integrations across enterprise applications, data platforms, and operational systems
Experience leading cross-functional technical work and influencing architecture and delivery decisions without relying solely on direct authority
Must not require Intel immigration sponsorship
Advanced communication skills to tailor complex technical content for engineering, planning, and executive audiences
Tolerance for responsible experimentation, including disciplined learning from approaches that do not succeed
Preferred: Master's degree in a relevant technical or quantitative discipline
Preferred: Experience in semiconductors, electronics, or comparable high-volume manufacturing environments
Preferred: Working knowledge of supply planning, demand planning, SnOP or integrated business planning, capacity planning, inventory optimization, procurement, and order management
Preferred: Experience with SAP, Kinaxis Rapid Response, Oracle, manufacturing execution systems, or equivalent technologies
Preferred: Experience with cloud data and AI platforms, distributed data processing, enterprise-grade observability, and security
Preferred: Experience with large language models, retrieval-augmented generation, agentic frameworks, and evaluation and governance of generative AI solutions
Preferred: Experience building interactive scenario-modeling or decision-support applications used by operational teams
Preferred: Experience hiring, mentoring, and technically leading AI, machine-learning, or data-engineering talent
Preferred: APICS CPIM, CSCP, or equivalent supply-chain certification

Benefits

Comp & perks

Competitive pay
Stock bonuses
Health benefits
Retirement benefits
Vacation benefits
Hybrid work model allowing employees to split time between working on-site and off-site