Quick Summary
Shape and execute a unified product roadmap spanning the Analyser hardware units, AI/computer vision data extraction, the Analyser operator portal, and the Deepnest packaging intelligence platform.
Lead and develop a lean, high-impact product management and design team, fostering a culture of high accountability, rapid experimentation, and structured delivery Data-Driven Decision Making: Define,
At Greyparrot, we are on a mission to solve the global waste crisis by introducing transparency and automation to a traditionally opaque sector. As the pioneer of AI-powered waste intelligence, our technology processes billions of waste objects, turning trash into actionable data.
Through our hardware-enabled SaaS model—comprising the Greyparrot Analyzer (IoT computer vision units deployed over recycling conveyor belts) and Deepnest (our next-generation packaging intelligence platform)—we empower recycling facilities (MRFs), fast-moving consumer goods (FMCG) giants like Kenvue, Unilever, and L’Oréal, and global regulators to track packaging performance, maximize recovery, and turn circular economy aspirations into financial and operational reality.
The world is in a waste crisis. Currently we produce 2.1 billion tons of solid waste per year. Data collection of the waste we produce is non-existent, meaning no systematic transparency and no accountability. It means that recycling targets are not upheld, dumping of waste into our oceans remains nobody's responsibility, recyclables get sent to landfill or incineration, and producers get away with sub-standard packaging. Thus, recycling rates stubbornly remain at 10% and, unless we change, by 2040 the plastic stock in the ocean will have quadrupled - a problem that already costs society $1.5 trillion each year.
Our mission is to digital waste flows to accelerate the circular economy. Currently, our camera system and AI software are deployed in recycling plants and waste facilities around the world to measure material flows and provide waste analytics. We have compiled a team of experts to deploy our technology and we’re looking to expand our team.
About the Role
~1 min readAs our VP of Product, you will own the vision, strategy, and execution of Greyparrot’s dual-pronged product ecosystem. Your work will bridge the gap between sophisticated hardware deployment and deep, data-driven AI software platforms.
This is a pivotal executive role for a platform thinker. Greyparrot operates across two distinct buyer markets - recycling facility operators and the brands and regulators who depend on packaging performance data - served through a single underlying AI intelligence platform. The VP of Product owns the roadmap and architecture logic that connects these two surfaces, ensuring the right sequencing of investment so that growth in one directly compounds the value delivered by the other. This requires deep technical fluency in AI and data pipelines, commercial instinct across two very different buyer types, and the strategic clarity to build a coherent platform while serving audiences with fundamentally different needs.
This role will be based in our London office at least one day a week, and reports directly to Ambrish, our Cofounder & CPO.
Responsibilities
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Product Vision & Strategy: Shape and execute a unified product roadmap spanning the Analyser hardware units, AI/computer vision data extraction, the Analyser operator portal, and the Deepnest packaging intelligence platform.
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Platform Roadmap Ownership: Own the data platform architecture and roadmap that connects Analyser output to Deepnest intelligence. Partner with the Deepnest PM on that product's surface-level execution while retaining strategic ownership of the platform layer beneath it.
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Product & Design Integrity: Ensure all product design and UX decisions are grounded in available data models and technical reality before reaching engineering. You are accountable for the quality and implementability of product specifications - not just the vision behind them.
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Cross-Functional Leadership: Act as a peer to Engineering and AI Research - translating commercial requirements into technically grounded product decisions, and ensuring the product team never creates work that engineering cannot build.
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Team Leadership & Mentorship: Lead and develop a lean, high-impact product management and design team, fostering a culture of high accountability, rapid experimentation, and structured delivery
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Data-Driven Decision Making: Define, track, and optimise core product KPIs, driving product-led growth (PLG) indicators, client retention, and data-accuracy features.
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Customer Advocacy: Act as the voice of the customer—spending time on-site at recycling facilities as well as in the boardrooms of enterprise sustainability and operations executives to anticipate market needs before they arise.
8+ years of product management experience, with at least 3+ years in a senior leadership role (VP, Head of Product, or Director) within a fast-growing B2B tech scale-up.
Proven experience scaling a data platform product where one product's output feeds another's value proposition — you understand how to sequence roadmaps across upstream and downstream dependencies, and how to maintain a coherent platform architecture while serving distinct buyer personas on top of it.
Deep technical fluency in machine learning lifecycles, computer vision, data labeling pipelines, and edge-AI computing. You will be expected to work within technical constraints, not around them - grounding product decisions in what is actually buildable with available data, and challenging AI researchers on accuracy, model drift, and data ingestion metrics.
Experience and understanding of the unique friction of physical supply chains, hardware retrofitting, firmware updates, and how they interact with cloud-based software layers.
Strong background in owning B2B data products that serve multiple distinct buyer types - you understand how to translate the same underlying dataset into different value propositions for different audiences, without fragmenting the platform beneath them.
Experience defining monetization strategies for hybrid models (e.g., upfront hardware costs paired with recurring SaaS data subscriptions).
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Location & Eligibility
Listing Details
- First seen
- June 12, 2026
- Last seen
- June 13, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 51%
- Scored at
- June 12, 2026
Signal breakdown
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