Senior Knowledge Manager IC4

Responsibilities

• Produce a roll out a written Knowledge Strategy: Endorsed by the executive, this should explicitly address how we do knowledge management in an AI-enabled world.
• Separate knowledge from working documents: Define what counts as knowledge for the Group (content that is authoritative, owned, current and worth trusting) versus working documentation such as drafts, scratchpads, meeting notes and in-flight thinking, set a higher bar for publishing to the knowledge corpus and use LLM-based tools to help enforce it.
• Actively maintain the corpus: Set review cycles, ownership and freshness expectations across the corpus and run the rhythm that keeps them honest, surface and resolve stale, duplicate or contradictory content (using LLM-based tools to scan for out-of-date facts, flag contradictions and identify duplicates across thousands of pages), treat decommissioning as a first-class activity with a credible first pass at the existing estate inside six months, maintain clear lineage, and work with Legal, Risk and Compliance on access, retention and regulator-facing obligations across our markets.
• Fill the gaps in critical domains: Map the domains across the Group, identify where authoritative knowledge is thin or missing, and work down a prioritised backlog through a federated maintainer model where each significant domain has a named expert and a named maintainer working to a standard you set and a cadence you run, sitting alongside teams during the moments that generate important knowledge such as launches, incidents, regulatory responses and M&A so the output is captured at the time, not retrofitted later.
• Make compounding learning a habit: Establish a simple, mandatory pattern for every significant piece of work to leave behind structured learning (what was the question, what did we decide, what happened, what would we do differently), make that the default output of quarterly business reviews, retros, incident washups, experiments and major decisions rather than a separate task that gets dropped, and curate the resulting record so it is actually retrievable later by people and by AI tools.
• Equip our knowledge for AI consumption: Be the person who understands, in practical terms, how modern AI systems consume organisational knowledge (retrieval, structure, metadata, permissions) and who specifies what good looks like from the knowledge side, working with the AI Lead, Chief Architect, Chief Data Officer and Engineering on how knowledge is indexed, retrieved and served into internal AI tools (you own the content and its structure; they own the systems that consume it). You are not expected to build production retrieval systems yourself, but to know enough to make sensible design decisions, write the requirements, and spot when an AI answer is grounded in the wrong source.
• Roll your sleeves up: Author and maintain the Group's canonical knowledge index: the single place any employee or internal AI tool goes to find the authoritative source on a topic.
• Skills we'd love you to have
• This role suits someone who brings order to complexity, is comfort

Additional Information

Senior Knowledge Manager Knowledge is becoming a source of competitive advantage. Organisations that have organised, current and trusted knowledge, usable by both people and AI, will make better decisions, move faster, and serve customers better than those that do not. We want our knowledge to be a quiet advantage for ClearScore Group, not a tax. This role exists to do two things at once. The first is hands-on. We have an ever-growing set of working documents, Notion pages, dashboards, and content across a cloud drive, SharePoint and Slack. Some of it should no longer exist. Some of it is foundational and is at risk of being lost. Someone needs to roll their sleeves up: write, rewrite, restructure, decommission, and own the canonical index of what we actually know. This is part librarian, part documentarian, part data steward. The second is strategic. We need to stop treating our corpus of working documents as if it were authoritative knowledge, build a deliberate corpus that is, maintain it actively as facts change, fill the gaps in critical domains like products, regulation and markets, and make sure every piece of significant work leaves behind reusable learning. This is the knowledge architect side of the role. The person in this seat owns the standards, owns the canonical index, does the work where it has to be done by them, and gets the rest done through a federated network of domain owners and maintainers across the Group. This is a mid-level individual contributor role with no direct reports. It sits centrally and reports to the VP of Operations who reports directly to the CEO. The reporting line is deliberately function-agnostic: not inside Data, not inside Architecture, and not inside AI Engineering. This is so the role can set standards across all of those functions without being captured by any of them.

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