Head of Product in London

Requirements

• Product management experience: 6+ years in product management, ideally in AI/ML, data science or intelligence-related fields, with a proven track record of shipping complex products from concept through to delivery.
• Industry expertise: Experience in threat intelligence, risk analysis or geopolitical intelligence, or in other highly regulated / security-sensitive environments (e.g. government, defence).
• Product leadership: Demonstrated experience leading product development in a startup or growth-stage company, with a proven ability to build and scale products and processes effectively.
• Advanced technical skills: A foundation in machine learning, data analytics or AI product management, particularly integrating complex ML models into user-facing products.
• Analytical and strategic thinking: Able to translate complex technical solutions into strategic product decisions, judging when user research should lead and when expertise-led conviction should, using data and analytics as inputs rather than the whole answer.
• Agile product management: Familiarity with agile methodologies and experience establishing agile processes in a startup or high-growth environment.
• Technical and AI/ML fluency: Comfortable working hand-in-hand with engineering and ML teams, with a good understanding of how complex ML models are integrated into user-facing products, alongside data processing and analytics.
• Cross-functional collaboration: Skilled at working with engineering, ML, design and customer success, and at synthesising input from multiple disciplines into a coherent product direction in a collaborative, agile environment.
• Communication: Excellent written and verbal communication, with the ability to present complex information clearly to executives, clients and internal teams.

What the job involves

We're looking for a Head of Product to own and lead product management at ExTrac. You’ll own our AI-driven products end-to-end – strategy, roadmap, discovery, research, requirements and delivery – and build the product function that supports them. Reporting directly to the CEO, and working closely with the design, engineering, research & analysis and wider leadership teams, you’ll synthesise input from across the company into a clear product direction, agree it with the founders, and make sure the team is always working on the right things at the right time.

• Product strategy and roadmap: Working closely with the CEO, develop, refine and execute the product roadmap, aligning it with ExTrac’s strategic objectives and client needs. Develop the product vision and strategy for our AI-driven products, agree it with the founders, and then own its delivery – balancing user needs, business goals, BD requirements and specific client requirements. Support both long‑term strategic planning and short‑term sprint execution. Stay across all product and engineering work in flight – who is working on what, expected timelines, and what’s paused, delayed or blocked. Stay close to industry trends and competitor products, especially in AI and B2B SaaS.
• Discovery, definition and delivery: Lead products end-to-end, from concept and planning through to delivery. Translate visions, feature requests and ideas into fully developed product briefs – leading discovery, pushing for clarity and further discussion where needed, and writing and maintaining clear, actionable PRDs. Conduct investigations, gather information and assess feasibility. Keep all stakeholders aligned and up to date as plans and requirements evolve.
• Technical and data leadership: Apply technical judgement to guide product development, especially around AI/ML model integration, data processing and analytics. Translate complex technical solutions into clear, strategic product decisions. Use behavioural data and analytics as a key input to decisions and prioritisation, alongside domain expertise and conviction.
• Research, validation and data: Establish usage analytics and behavioural data to track adoption and surface friction, and build workflows to analyse and act on it. Favour observation over interrogation – watching people work usually tells you more than asking them what they want. Use user research and testing for what it does well – validating legibility, usability, and workflow fit. This evaluative feedback should carry real weight and is often decisive; workflow fit deserves particular care given our high‑stakes users. Recognise the limits of research when it comes to direction: what we should build is synthesised from deep expertise across intelligence analysis, subject‑matter, data science, engineering and design. For bets that can’t be validated up front, apply post‑deployment discipline: define success metrics and evaluation timelines, commit in advance to the evidence that would change your mind, and assess honestly whether the bet is paying off. Organise and run internal alpha and beta testing, plus external testing where possible; partner with Customer Success to recruit proxy users and to surface the friction, workflow issues and pain points that feed into execution and prioritisation.
• Working with AI: Partner closely with the AI team to ensure AI features are genuinely useful, understandable and aligned with what our users need. Help track and document the specific behaviours and styles built into Co‑Analyst responses. Support the work to adapt Co‑Analyst to the needs of specific external organisations.
• Building and leading the product function: Build and formalise the product function – its agile processes, road‑mapping systems and prioritisation frameworks. Establish metrics to track product success, adoption and customer satisfaction. Grow and mentor a high‑performing product team as ExTrac scales, fostering a culture of innovation and collaboration.
• Cross‑functional collaboration: Act as the primary link between product, engineering, machine learning, design, customer success and research, ensuring cohesive development cycles and smooth delivery. Partner closely with the design team on design and UX, engineering on build and AI/ML, and the research & analysis team on domain expertise – synthesising their input into a coherent product direction. Facilitate cross‑team workshops to bring evidence and expertise together, and strengthen pre‑sprint and in‑sprint communication.