Investment Research Domain & Workflows 

• Develop deep domain knowledge of the research lifecycle — idea generation, authoring, editing, supervisory/compliance review, publishing, entitlements, and distribution to clients and internal consumers. 

• Conduct structured analysis of Research workflows, content pipelines, and user interaction patterns to identify inefficiencies, manual bottlenecks, and opportunities for automation and quality uplift. 

• Partner with Research management, analysts, and product owners to capture requirements and help shape the roadmap for Research systems. 

• Map current-state and target-state processes; document business rules, edge cases, and hand-offs across the research value chain.
   

Data Analysis & Requirements 

• Conduct hands-on data exploration, profiling, and quality analysis across research content, market data, reference/entity data, and metadata to validate feasibility and surface data gaps. 

• Define and maintain data requirements, data dictionaries, taxonomies, ontologies, and metadata standards

• Partner with Data Engineering to identify data quality issues, lineage requirements, and enrichment opportunities. 

• Translate business needs into clear, testable requirements — process flows, functional specifications, user stories, data specs, and acceptance criteria

Project & Delivery Management 

• Plan and manage delivery of Research IT initiatives end-to-end — defining scope, milestones, timelines, resourcing needs, and dependencies across multiple squads and vendors. 

• Maintain project plans, delivery roadmaps, RAID logs (risks, assumptions, issues, dependencies), and status reporting for stakeholders and management. 

• Drive day-to-day execution — coordinating engineering, data, UI/UX, and TechOps teams, running stand-ups/working sessions, and removing blockers to keep delivery on track. 

• Track budget, effort, and progress against plan; proactively escalate risks and manage change to protect scope, timeline, and quality. 

• Coordinate releases, cutover, and go-live readiness, ensuring stakeholder sign-off and smooth transition to support/BAU. 

• Provide clear, concise updates to senior stakeholders (including ExCo/SteerCo-level reporting) on delivery status, risks, and decisions required.

• Identify where AI can deliver measurable productivity and quality improvements within research workflows — e.g. drafting support, summarisation, tagging/classification, newsfeed intelligence, and recommendations. 

• Support proof-of-concept validation and evaluation of AI features, including test sets, scoring approaches, and acceptance criteria, so AI is applied pragmatically and safely. 

• Help define prompt/response expectations, guardrails, and human-in-the-loop review steps where AI is embedded into workflows. 
   

Governance & Controls 

• Coordinate UAT, human-in-the-loop reviews, and release readiness with business users and technical teams. 

• Partner with Compliance and Risk to ensure regulatory requirements, data residency, and control requirements are embedded into workflows and system changes.