Location
London

Hours
Full Time

Salary
Negotiable

About the Role
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: managing an ever-growing set of working documents, Notion pages, dashboards, and content across cloud drives, SharePoint and Slack. Some content should no longer exist, some is foundational and at risk of being lost. You will 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: to stop treating working documents as authoritative knowledge, build a deliberate corpus that is authoritative, maintain it actively as facts change, fill gaps in critical domains like products, regulation and markets, and ensure every significant piece of work leaves behind reusable learning. You will own the standards, the canonical index, do the work where necessary, and coordinate a federated network of domain owners and maintainers across the Group.

This is a mid-level individual contributor role with no direct reports. It reports centrally to the VP of Operations, who reports directly to the CEO. The reporting line is function-agnostic to enable setting standards across Data, Architecture, and AI Engineering without being captured by any single function.

Key Responsibilities:
1. Produce and roll out a written Knowledge Strategy endorsed by the executive, explicitly addressing knowledge management in an AI-enabled world.
2. Define what counts as knowledge versus working documentation, set a higher bar for publishing to the knowledge corpus, and use LLM-based tools to enforce standards.
3. Actively maintain the corpus by setting review cycles, ownership, freshness expectations, resolving stale, duplicate or contradictory content, and managing decommissioning.
4. Map domains across the Group, identify knowledge gaps, and work through a federated maintainer model to fill these gaps.
5. Establish a mandatory pattern for every significant piece of work to leave behind structured learning, making it the default output of reviews, retrospectives, incidents, experiments, and decisions.
6. Equip knowledge for AI consumption by understanding how AI systems consume organisational knowledge and specifying requirements for indexing, retrieval, and serving content.
7. Author and maintain the Group's canonical knowledge index as the single authoritative source for employees and internal AI tools.

Experience
- Proven track record of materially improving knowledge organisation, ownership, and maintenance in mid-to-large organisations through strategy, standards, and hands-on work.
- Comfortable with data governance concepts applied to knowledge including ownership, custodianship, classification, access, lineage, and retention.
- Awareness of obligations in regulated markets.
- Practical understanding of how modern AI tools consume organisational knowledge and ability to specify requirements and evaluate AI outputs.
- Fluent across tooling landscapes such as wikis, documentation systems, knowledge bases, document stores, and search.
- Experience influencing without formal authority and managing federated networks of maintainers.
- Bias to action with willingness to do hands-on work like writing, fixing links, renaming files, restructuring folders, and archiving outdated content.

About You
- Able to bring order to complexity and operate effectively across functions.
- Strong writer and content structurer capable of turning messy source material into clear, accurate, well-structured content.
- Able to design usable taxonomies and indexes.
- Constructive and resilient when holding standards and saying no.
- Comfortable working in a fast-paced, evolving environment with a bias to action.

Qualifications
- Technical depth can be grown into; judgement and bias to action are essential.
- Desirable: Experience in financial services, fintech, or regulated industries with documentation and records obligations.
- Desirable: Background in library or information science, technical writing, knowledge engineering, records management, or data governance.
- Desirable: Experience operating federated models with appointed experts and maintainers.
- Desirable: Exposure to API-led businesses and organisations transitioning to AI-assisted knowledge retrieval.