Intent HQ Limited
If you're passionate about AI, product strategy, and translating complex models into customer value, this is your chance to own a core technical moat and shape what comes next! Role Context This role sits within the Product & Tech organisation at Intent and partners closely with Data Science, Engineering, Client Services (to design and maintain quality standards) and other Product Managers. IntentAI is the inference layer that turns raw behavioural and device signals into the brands, topics, moments and, eventually, forward-looking predictions (move, buy, churn) that make IntentOne valuable. IntentAI includes an Intent Semantic Layer that selects the right context for each goal and agent at scale, and is our core technical moat. Today that moat is real but under-explained. This role owns that gap: setting the model north star, raising the bar on model quality and measurement, and making the intelligence legible to buyers, sellers, and the team. Role Summary This role is responsible for: Owning the product roadmap and north star for IntentAI: what our models predict, how good they need to be, and how that quality compounds into commercial value. Harmonising the Intent Semantic Layer across device and cloud signals, so behaviour captured on the device and in the cloud resolves into one consistent, trustworthy view of human context. Setting and operationalising the quality bar for models and enrichments (coverage, precision, recall, lift, freshness, drift, and cost at scale) so model performance is measured, visible, and improving. This role will be heavily data- and metric-driven. Owning the product story of the semantic layer: explaining clearly why it is differentiated, how it makes sense of human context against a goal, and why it is hard to replicate. Translating model and signal capability into outputs customers trust, with explainability and confidence built in rather than bolted on. Partnering with Data Science and Engineering to prioritise the model roadmap, close evaluation and monitoring gaps, and turn research into shipped, reliable product. Core Responsibilities Own the model north star and vision Define what "good" looks like across the model portfolio (brand and topic affinity, intent segments, moments of need, propensity and churn), with a clear understanding of how it ladders to customer ROI. Maintain a prioritised model roadmap grounded in commercial impact, not research novelty, and make the hard calls on where signal and model depth create real advantage. Articulate a durable vision for how the intelligence layer compounds, with better signals and scale driving better models at lower unit cost. Raise and operationalise model quality Set the evaluation standard for inference quality, coverage, cost, and scalability, measured by lift, precision, recall, and proxy outcomes against ground truth and in-market results. Work with QA to close known gaps in evaluation, drift detection, and performance monitoring so quality is continuously observed, not discovered in incidents. Define maturity indicators, confidence thresholds, and suppression rules so we never ship outputs that are misleading or unexplained. Explain the intent semantic layer Own clear, layered explanations of the semantic layer for technical and non-technical audiences, covering how it harmonises device and cloud signals, selects relevant context per goal, and keeps the platform accurate and cost-efficient at billions of events. Turn the moat into language Sales and Solutions can use to connect the smarts of the platform to the customer promise, without overstating what the evidence supports. Make outputs trustworthy Ensure outputs are grounded in model evidence, with explainable scoring and clear confidence, so enterprise buyers trust how they are generated and how their own rules are handled, removing the "black box" objection. Translate model needs into clear product requirements and reusable platform capability with Data Science and Engineering, and own the tradeoffs across model richness, latency, cost, and time-to-value. What you'll bring Deep fluency across the modelling stack, from supervised learning, propensity and uplift, to representation and embedding models, alongside modern LLM-based systems. You understand how predictive models are trained, evaluated, and shipped, not just prompted. A metrics-first instinct: fluent in precision, recall, lift, AUC, drift, and the economics of running models at scale. You explain complex intelligence simply and hold a high bar without stalling delivery. An exceptional flair for data translation and storytelling. You can turn complex models, signals, and analytical outputs into clear, compelling narratives that make the intelligence intuitive, credible, and actionable for technical and non-technical audiences alike. 26 days holiday (increasing with service) Most companies lose the detail. A large mobile network generates hundreds of billions of events every single day - too expensive to keep in raw form. So your rich, specific, wonderfully complicated life gets compressed into an average. You become "males 25-34 in the southeast who browsed upgrades." A bucket. A segment. And then that bucket gets blasted with messages - because when you cannot tell what someone actually wants, the only strategy left is frequency. Send more, send often, hope something lands. We built something different. Intent runs two types of intelligence in parallel. Our cloud engine reads billions of events a day - weblogs, payments, app signals, CRM records - and finds behavioural sequences: chains of small actions that unfold in a recognisable order before someone switches, upgrades, or leaves. Our on-device Edge AI sits directly on the phone, reading 16,000 signals and distilling them into 750 insights about your current state - without any of that information ever leaving the device. We manage over 300 million customer profiles. We underpin $115 billion in customer value. We hold 49 patents in behavioural AI, privacy architecture, and on-device intelligence. We were named AI Business of the Year 2025. And Verizon - with large data science teams of its own - has been our customer for seven years, because what we have built over a decade of patents, learning, and rigorous product development cannot be easily replicated. Our Team Around 110 people and growing, based across London, Barcelona, Lisbon, New York, and Tel Aviv. Engineers, data scientists, product designers, and commercial minds who collectively speak over 15 languages. United by a single conviction: when you understand human behaviour properly, relevance follows - and when relevance follows, so does value. Why Join Intent Work that matters at genuine scale. Our clients have tens of millions of customers and billions of interaction events each day. You will not be optimising a landing page - you will be shaping how the world's largest enterprises understand and serve their customers. Category-defining technology. 49 patents. An AI that reads 16,000 behavioural signals directly on your phone - data never leaves. A cloud engine that digests 250 billion events a day and delivers fresh intelligence in under 120 seconds. Privacy twins that make re-identification mathematically infeasible. Global reach, real momentum. Verizon, MTN Group, ABG Capital - across telecoms, financial services, retail, and social media. Expanding rapidly into Latin America, Africa, APAC, India, and the Middle East. AI Business of the Year 2025. A place to grow - fast. We are at an inflexion point: approaching profitability, launching a flagship agentic platform, and scaling commercially across seven regions. The people who join now will shape the next chapter. Hybrid and flexible. London-based roles work from the office three days a week, with the rest remote. Team members across Barcelona, Lisbon, New York, South Africa, Nigeria, Brazil, India, and Tel Aviv - a genuinely international team solving genuinely global problems. Our Values - In Practice Human-first. No one is average. We start with individual reality, motivation, and context - not statistical convenience or abstract users. Principled. Privacy is a human right - a design principle, not a legal afterthought. Trust is foundational, not optional. Ambitious. We anticipate the edge - looking ahead early and building what others assume cannot yet be built. Inclusive. Intentionally relevant. Every interaction should earn its place - serving simply, with clarity in product, communication, and action. In service. We serve simply - to our customers, to each other, and to the work itself. No unnecessary complexity, no ego in the way.
If you're passionate about AI, product strategy, and translating complex models into customer value, this is your chance to own a core technical moat and shape what comes next! Role Context This role sits within the Product & Tech organisation at Intent and partners closely with Data Science, Engineering, Client Services (to design and maintain quality standards) and other Product Managers. IntentAI is the inference layer that turns raw behavioural and device signals into the brands, topics, moments and, eventually, forward-looking predictions (move, buy, churn) that make IntentOne valuable. IntentAI includes an Intent Semantic Layer that selects the right context for each goal and agent at scale, and is our core technical moat. Today that moat is real but under-explained. This role owns that gap: setting the model north star, raising the bar on model quality and measurement, and making the intelligence legible to buyers, sellers, and the team. Role Summary This role is responsible for: Owning the product roadmap and north star for IntentAI: what our models predict, how good they need to be, and how that quality compounds into commercial value. Harmonising the Intent Semantic Layer across device and cloud signals, so behaviour captured on the device and in the cloud resolves into one consistent, trustworthy view of human context. Setting and operationalising the quality bar for models and enrichments (coverage, precision, recall, lift, freshness, drift, and cost at scale) so model performance is measured, visible, and improving. This role will be heavily data- and metric-driven. Owning the product story of the semantic layer: explaining clearly why it is differentiated, how it makes sense of human context against a goal, and why it is hard to replicate. Translating model and signal capability into outputs customers trust, with explainability and confidence built in rather than bolted on. Partnering with Data Science and Engineering to prioritise the model roadmap, close evaluation and monitoring gaps, and turn research into shipped, reliable product. Core Responsibilities Own the model north star and vision Define what "good" looks like across the model portfolio (brand and topic affinity, intent segments, moments of need, propensity and churn), with a clear understanding of how it ladders to customer ROI. Maintain a prioritised model roadmap grounded in commercial impact, not research novelty, and make the hard calls on where signal and model depth create real advantage. Articulate a durable vision for how the intelligence layer compounds, with better signals and scale driving better models at lower unit cost. Raise and operationalise model quality Set the evaluation standard for inference quality, coverage, cost, and scalability, measured by lift, precision, recall, and proxy outcomes against ground truth and in-market results. Work with QA to close known gaps in evaluation, drift detection, and performance monitoring so quality is continuously observed, not discovered in incidents. Define maturity indicators, confidence thresholds, and suppression rules so we never ship outputs that are misleading or unexplained. Explain the intent semantic layer Own clear, layered explanations of the semantic layer for technical and non-technical audiences, covering how it harmonises device and cloud signals, selects relevant context per goal, and keeps the platform accurate and cost-efficient at billions of events. Turn the moat into language Sales and Solutions can use to connect the smarts of the platform to the customer promise, without overstating what the evidence supports. Make outputs trustworthy Ensure outputs are grounded in model evidence, with explainable scoring and clear confidence, so enterprise buyers trust how they are generated and how their own rules are handled, removing the "black box" objection. Translate model needs into clear product requirements and reusable platform capability with Data Science and Engineering, and own the tradeoffs across model richness, latency, cost, and time-to-value. What you'll bring Deep fluency across the modelling stack, from supervised learning, propensity and uplift, to representation and embedding models, alongside modern LLM-based systems. You understand how predictive models are trained, evaluated, and shipped, not just prompted. A metrics-first instinct: fluent in precision, recall, lift, AUC, drift, and the economics of running models at scale. You explain complex intelligence simply and hold a high bar without stalling delivery. An exceptional flair for data translation and storytelling. You can turn complex models, signals, and analytical outputs into clear, compelling narratives that make the intelligence intuitive, credible, and actionable for technical and non-technical audiences alike. 26 days holiday (increasing with service) Most companies lose the detail. A large mobile network generates hundreds of billions of events every single day - too expensive to keep in raw form. So your rich, specific, wonderfully complicated life gets compressed into an average. You become "males 25-34 in the southeast who browsed upgrades." A bucket. A segment. And then that bucket gets blasted with messages - because when you cannot tell what someone actually wants, the only strategy left is frequency. Send more, send often, hope something lands. We built something different. Intent runs two types of intelligence in parallel. Our cloud engine reads billions of events a day - weblogs, payments, app signals, CRM records - and finds behavioural sequences: chains of small actions that unfold in a recognisable order before someone switches, upgrades, or leaves. Our on-device Edge AI sits directly on the phone, reading 16,000 signals and distilling them into 750 insights about your current state - without any of that information ever leaving the device. We manage over 300 million customer profiles. We underpin $115 billion in customer value. We hold 49 patents in behavioural AI, privacy architecture, and on-device intelligence. We were named AI Business of the Year 2025. And Verizon - with large data science teams of its own - has been our customer for seven years, because what we have built over a decade of patents, learning, and rigorous product development cannot be easily replicated. Our Team Around 110 people and growing, based across London, Barcelona, Lisbon, New York, and Tel Aviv. Engineers, data scientists, product designers, and commercial minds who collectively speak over 15 languages. United by a single conviction: when you understand human behaviour properly, relevance follows - and when relevance follows, so does value. Why Join Intent Work that matters at genuine scale. Our clients have tens of millions of customers and billions of interaction events each day. You will not be optimising a landing page - you will be shaping how the world's largest enterprises understand and serve their customers. Category-defining technology. 49 patents. An AI that reads 16,000 behavioural signals directly on your phone - data never leaves. A cloud engine that digests 250 billion events a day and delivers fresh intelligence in under 120 seconds. Privacy twins that make re-identification mathematically infeasible. Global reach, real momentum. Verizon, MTN Group, ABG Capital - across telecoms, financial services, retail, and social media. Expanding rapidly into Latin America, Africa, APAC, India, and the Middle East. AI Business of the Year 2025. A place to grow - fast. We are at an inflexion point: approaching profitability, launching a flagship agentic platform, and scaling commercially across seven regions. The people who join now will shape the next chapter. Hybrid and flexible. London-based roles work from the office three days a week, with the rest remote. Team members across Barcelona, Lisbon, New York, South Africa, Nigeria, Brazil, India, and Tel Aviv - a genuinely international team solving genuinely global problems. Our Values - In Practice Human-first. No one is average. We start with individual reality, motivation, and context - not statistical convenience or abstract users. Principled. Privacy is a human right - a design principle, not a legal afterthought. Trust is foundational, not optional. Ambitious. We anticipate the edge - looking ahead early and building what others assume cannot yet be built. Inclusive. Intentionally relevant. Every interaction should earn its place - serving simply, with clarity in product, communication, and action. In service. We serve simply - to our customers, to each other, and to the work itself. No unnecessary complexity, no ego in the way.