IntelliSense.io is offering a great opportunity for a talented Product Manager to lead the development and delivery of Industrial AI agents for global Minerals and Metals operators. Today we are deploying AI-powered software solutions that drive real-time optimisation, delivering millions in value to some of the world’s largest industrial companies.
With a global team of 70 employees and offices across four continents, our HQ is in Cambridge, UK, with regional offices in Saudi Arabia, Chile, Australia, and Ireland.
About the Role
We are evolving our Industrial AI Operating System for the global mining sector from a suite of solutions into a unified platform. We’re looking for a product leader to own the foundational data architecture and cross-app consistency required for this transition. This role is ideal for an experienced professional who understands the critical impact of robust data strategy in industrial environments.
What You’ll Own
- Data In/Out Governance: Define and scale a robust data strategy covering integration standards, ingestion rules, and egress governance across all customer deployments.
- Cross-App Feature Strategy: Unify global optimization configurations and core features (equations, alerts, reports) into consistent specifications for engineering.
- Edge Deployment: Drive deployment of our Edge recommendation layer across sites, managing model drift, update cadences, and auto-pause safety controls.
- Platform Cost Reduction: Translate operational pain points into a prioritized roadmap that optimizes software performance and lowers the internal cost-to-mine.
What We’re Looking For
- 5+ years in enterprise software product management, owning a complex, multi-component product end-to-end
- Background in industrial, scientific, or process-industry software — mining, energy, manufacturing, chemicals, or similar
- Strong grasp of data integration patterns: APIs, event-driven architectures, ETL, and the governance challenges they bring
- Experience making cross-platform feature strategy decisions — what gets standardised, what gets customised, and why
- Able to move between strategic thinking and precise, buildable specifications without losing altitude
- Strong stakeholder management across engineering, sales, and customer success
- Experience working in highly automated, modern product operations environments
Nice to have
- Experience with edge computing or on-premise deployment in industrial environments
- Familiarity with ML model lifecycle management — drift detection, retraining, and what that means for end users
- Exposure to industrial data standards or ontologies (ISO 15926, OSDU, or similar)
- Experience working with mining, metallurgical, or geological domain experts
Interview Process
- Screening with the People Ops Team
- Tech Interview with the VP of Products
- Take-home test project
- Team Interview to present your test output
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