AI Product Stream Lead

Role Purpose The AI Product Stream Lead defines and delivers AI-powered supply chain solutions that solve real customer and operations problems on the Cargoo platform. You own a product stream end to end, from framing where AI creates value (automation, decision support, agents) to shipping it and proving it works in production. You lead a cross-functional team, balancing customer value, technical and data feasibility, AI quality and cost, business priorities and delivery timelines. You also help set how Cargoo builds product in an AI-native way. Key Responsibilities AI Product Vision & Strategy - Shape the stream’s product vision and strategy with stakeholders, in line with Cargoo’s AI strategy and customer needs. - Identify and prioritize AI opportunities across freight operations (e.g. exception handling, quoting, document processing, planning and execution support) by value, feasibility and risk. - Decide where AI should automate, where it should assist, and where a human must stay in control. - Own product decisions, balancing customer value, technical feasibility, AI quality, cost, business priorities and delivery timelines. Leadership & Delivery - Lead the development team through the whole product lifecycle, from discovery to rollout and adoption. - Decide scope, schedule and quality, including when an AI feature is good enough to ship and when it needs another iteration. - Run fast discovery loops: prototype with AI tools, validate with real users and data, then commit. - Make sure the team meets internal policies, data protection rules and responsible-AI standards (incl. EU AI Act awareness). Product Management & Requirements - Create clear Use Cases, Requirements and Business Rules. For AI features, add expected behaviour, edge cases, failure modes and escalation paths. - Define success and evaluation criteria for AI features with engineering: accuracy, latency, cost per task and acceptable error rates, backed by representative test sets. - Write User Stories and Acceptance Criteria that cover both deterministic logic and probabilistic AI behaviour. - Work with engineering and data owners on data readiness: which data we need, its quality, and who owns it. - Own and continuously refine the Product Backlog so the team always works on the highest-value opportunities. - Track adoption and impact after release (time saved, automation rate, user trust) and feed the learning back into the roadmap. AI-Native Ways of Working - Use AI tools every day for research, specs, prototyping, data analysis and feedback synthesis. - Help the team and wider product org adopt AI-assisted delivery practices. Candidate Profile Education & Experience - 5+ years managing software development projects, strongly preferably as a Product Owner. - Proven managerial skills: team leadership, decision-making and conflict resolution. - Degree or diploma in Information Technology, Informatics or a related field. Technical Skills (Must Have) - Deep understanding of Agile values, principles and frameworks (Scrum, Kanban, Lean) and the ability to apply them well. - Strong analytical skills, comfortable working with data to make product decisions. - Solid working understanding of how LLMs and AI agents work, what they are good at, and where they fail (hallucinations, non-determinism, cost, latency). - Hands-on, daily use of AI tools in your own work. - Fluent English, written and spoken. Strong Plus - Having shipped at least one AI/ML or LLM-based feature to production. - Experience defining evaluation criteria or test sets for AI features. - Logistics background or experience with logistics solutions. - Experience with data products, workflow automation or decision-support systems. Behavioral Traits - Excellent communication with technical and non-technical audiences, including customers, executives, managers and subject matter experts. Able to explain AI trade-offs in plain language. - Strong leadership and decision-making, able to guide teams and resolve conflicts. - Analytical, structured and value-driven in prioritization. Evidence over hype. - Curious and fast-learning, comfortable with ambiguity and quick iteration.