Data Transformation Consultant
R&D Data Flows £500-525 p/d Inside IR35 Hybrid - 1 dpw in London 5 months initial, extension expected
We are recruiting a Data Transformation Consultant to support a major transformation programme within the Research and Development function of a global life sciences organisation. The role will lead discovery and design activity, mapping existing processes and data flows before developing practical future-state solutions. You will use AI-enabled approaches to analyse existing documentation, accelerate discovery and reduce reliance on subject-matter experts. Role responsibilities Plan and lead discovery and future-state design sprints across R&D workstreams. Coordinate subject-matter experts, process owners and wider programme stakeholders. Use AI-enabled approaches to analyse documentation and create initial current-state process maps. Map processes, data flows, systems and dependencies, identifying gaps, duplication and inefficiencies. Design future-state processes, data flows and operating approaches, including opportunities for automation. Facilitate workshops and validate findings, assumptions and recommendations with stakeholders. Maintain delivery plans and manage risks, actions, issues, dependencies and progress reporting.
Key experience Senior-level consulting experience delivering transformation programmes within the pharmaceutical or life sciences sector, ideally across R&D functions. Demonstrable experience applying AI to accelerate discovery, analyse existing information or support transformation delivery. Strong business-analysis skills, including requirements gathering and mapping processes, data flows and dependencies. Project-management or workstream-delivery experience, including planning, coordination, reporting and RAID management. Experience in process redesign, operating-model development, data governance or AI-enabled automation. Strong stakeholder-management and workshop-facilitation skills across varied business and technical groups. Ability to work through complexity and translate detailed findings into clear recommendations and practical future-state designs.