Cognite - AI for Industry - Supply Chain Tech - Center of Excellence

• Solution & CoE Strategy: Align the CoE on high-impact use cases (e.g., delayed inbound spare → maintenance delay → order risk) and deploy them as demos or customer solutions. • Solution & CoE Strategy: • Knowledge-Graph Data Modeling: Lead customer modeling sessions; show how domain data models and complex hierarchies absorb requirements securely and at scale. • Knowledge-Graph Data Modeling: • Data & AI Architecture: Guide customers on adopting our graph/AI/UI stack, including integration patterns (Snowflake, Databricks, ERPs) and why agents must be grounded in deterministic graph paths. • Data & AI Architecture: • AI Skill-System Authorship: Turn domain expertise into structured AI skills with fixed parameters, guardrails, and testable anchors, keeping data models and agent logic aligned. • AI Skill-System Authorship: • Agentic Tooling & QA: Serve as escalation point for complex graph queries and agent/UI design; own testing and evaluation frameworks, classifying failures rigorously before changing agent logic. • Agentic Tooling & QA: • Enterprise Architecture & Value Selling: Support end-to-end architecture reviews and quantify commercial impact (orders at risk, delay penalties, landed costs) for executive business cases. • Enterprise Architecture & Value Selling: • Partner Enablement & Documentation: Equip partners with reusable demos and accelerators; maintain clear, consistent terminology across models, docs, and AI logic. • Partner Enablement & Documentation: • Deep knowledge of industrial supply chain planning/execution and its links to production, quality, and maintenance; fluency with ISA-95/88 and SCOR frameworks. • 3+ years in industrial supply chain (planning, logistics, inventory) and 5+ years in pre-sales, solution architecture, or delivery for enterprise software. • Experience designing industrial information/knowledge-graph data models (temporal relations, typed hierarchies) and integrating structured/unstructured enterprise data. • Hands-on proficiency in Python and frontend (TypeScript/React), with strong engineering discipline (ADRs, automated testing, CI/CD). • Systemic AI/testing mindset: optimized tool routing, rigorous multi-stage evaluation, root-cause debugging, and graceful handling of edge cases — not just prompting. • Excellent client-facing skills, able to translate complex data/AI concepts for audiences from engineers to C-level executives. • Familiarity with modern data platforms (Snowflake, Databricks, Spark/SQL), source systems (ERP/MES/WMS/TMS), and agentic AI/LLM workflow design. • Prior experience in high-growth (startup/scale-up) environments; Bachelor's degree or equivalent practical experience.