Lead Product Manager

The Role

We are looking for a Lead Product Manager to own the end-to-end delivery of major areas of Ex Trac's AI-driven products.

Reporting into Ex Trac's product leadership, and to the Head of Product once that role is in post, you will work closely with the design, engineering, research and analysis, and customer success teams to take significant product areas from ambiguity to shipped: leading discovery, defining requirements, driving delivery, and bringing order to the tools and processes that product work runs on.

Ex Trac is a founder-led, vision-led company.

This role is not about inventing the vision; it is about making it real: owning prioritisation within your product areas, keeping stakeholders aligned as plans evolve, and acting as a multiplier for product craft across the teams you work with.

• What the job involves
• Roadmap ownership and prioritisation
• Own the roadmap for one or more significant product areas, translating the strategy set by Ex Trac's founders and product leadership into a clear, sequenced, deliverable plan.
• Own prioritisation within your areas, balancing user needs, business goals, BD requirements, and specific client requirements, and defending those calls with evidence and reasoning.
• Stay across all work in flight within your areas: who is working on what, expected timelines, and what is paused, delayed, or blocked.
• Stay close to industry trends and competitor products, especially in AI and B2B Saa S, and feed that intelligence into roadmap discussions.
• Discovery, definition and delivery
• Lead products end to end within your areas, 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.
• Product operations and delivery
• Bring order to the tools and workflows that product development runs on (issue tracking, documentation, sprint and delivery processes), so they reflect how we actually work and scale as we grow.
• Own the interface between product management and delivery management within your areas: sequencing, dependencies, and visible progress against commitments.
• Establish the working hygiene (clearly structured initiatives, honest status, well-maintained backlogs) that lets engineering, design, and leadership trust the plan.
• Technical and data judgement
• Apply technical judgement to guide product development within your areas, especially around AI/ML model integration, data processing, and analytics.
• Translate complex technical solutions into clear product decisions and communicate the trade-offs to stakeholders.
• Use behavioural data and analytics as a key input to decisions and prioritisation, alongside domain expertise and conviction.
• Research, validation and data
• Use usage analytics and behavioural data to track adoption and surface friction within your product areas, 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 cannot 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: Co-Analyst
• 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.
• Raising the bar for product practice
• Contribute to the product function's processes, roadmapping systems, and prioritisation frameworks, and model their consistent use.
• Help define and track the metrics that indicate product success, adoption, and customer satisfaction within your areas.
• Act as a coaching and advisory resource for colleagues, sharing product craft across teams and mentoring less experienced team members as the function grows.
• Cross-functional collaboration
• Act as the connective tissue between product, engineering, machine learning, design, customer success, and research within your product areas, 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 and analysis team on domain expertise, synthesising their input into coherent product decisions.
• Facilitate cross-team workshops to bring evidence and expertise together, and strengthen pre-sprint and in-sprint communication.
• You should apply if
• You have shipped complex products in technical domains, ideally AI/ML, data, or intelligence, and you do your best work owning a product area end to end: from framing the problem through to measuring what shipped.
• You are as comfortable in the detail as in the plan.

Writing a PRD, interrogating usage data, or pressure-testing a model integration with engineers is the core of your craft, not a chore.

• You hold evidence and conviction in balance. You know when user research should lead and when expertise-led judgement should, and you can make and defend either call.
• You do your best work in a founder-led, vision-led company.

You know the difference between owning a vision and making a vision real, and the second is the work you enjoy.

The pace and ambiguity of a growth-stage company is what you are looking for rather than what you are tolerating.

• You find genuine satisfaction in bringing order to messy tooling and processes, because you have seen how much leverage a well-run product operation creates.
• You make the people around you better. You share your craft, coach colleagues, and improve the processes you work within, without needing a title that says you must.
• Where this role can take you
• Deepen your ownership. Strong performance means broader product areas, higher-stakes bets, and more direct influence over product strategy.
• Help define how AI-native intelligence products get built.

Co-Analyst is a category-shaping product, and the work you deliver will influence how the industry approaches human-AI analytical workflows.

• Grow with the product function.

As Ex Trac scales, this role is the natural path towards senior product leadership.

Progress on either track: Ex Trac values the individual contributor and Manager paths equally, so you can deepen technical scope or take on people leadership as your responsibilities grow.

Requirements

• Ex Trac provides services to a number of government clients, some of which specify nationality criteria for individuals seeking security clearance.

For this reason, we can only consider applications from individuals who are nationals of the UK, another NATO member state, Australia or New Zealand.

• 4+ 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.
• Experience delivering product work in a startup or growth-stage company, with demonstrated ability to own a product area end to end with minimal oversight.
• Able to translate complex technical solutions into product decisions, judging when user research should lead (usability and workflow fit) and when expertise-led conviction should (what to build), and using data and analytics as inputs rather than the whole answer.
• 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.
• Skilled at working with engineering, ML, design, and customer success, and at synthesising input from multiple disciplines into coherent product decisions in a collaborative, agile environment.
• Excellent written and verbal communication, with the ability to present complex information clearly to executives, clients, and internal teams.
• Experience in threat intelligence, risk analysis, or geopolitical intelligence, or in other highly regulated or security-sensitive environments (e. g. government, defence).
• A foundation in machine learning, data analytics, or AI product management, particularly integrating complex ML models into user-facing products.
• Familiarity with agile methodologies and experience working within established agile processes in a startup or high-growth environment.
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