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.