I&P GN - I&E - Life Science – R&D Consultant - Process Excellence-PE

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Requirements Summary

Working knowledge of one or more Life Sciences R&D sub-functions and their data: General R&D (target identification, drug discovery, R&D portfolio management, translational research); Clinical (prot

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OtherConsultant

AI Transformation Advisory: AI opportunity identification and qualification across Life Sciences R&D functions; AI-ready process design and solution shaping for drug discovery, clinical development, and regulatory workflows; experience conducting client workshops and capability assessments in Life Sciences context
Life Sciences R&D Domain Expertise (AI fluent): Working knowledge of one or more Life Sciences R&D sub-functions and their data: General R&D (target identification, drug discovery, R&D portfolio management, translational research); Clinical (protocol development, site selection, patient recruitment, clinical data management, EDC, CTMS, eTMF and other systems); Safety & Regulatory (pharmacovigilance, adverse event management, signal detection, regulatory submissions – IND/NDA/BLA/CTD, labeling, regulatory intelligence etc.)
Process Excellence + AI (R&D): Process discovery and redesign across R&D sub-functions; business process modelling for clinical operations, regulatory submissions, and safety workflows; process analysis and optimization with knowledge of GxP compliance requirements and validation considerations
Life Sciences R&D Functional Transformation: Functional process knowledge across R&D value chain – study start-up, clinical trial management, pharmacovigilance operations, regulatory affairs, and medical writing; familiarity with key platforms such as Veeva Vault (CTMS, RIM, Safety, eTMF), Medidata Rave, Argus Safety etc.
AI Value Architecture (R&D): Value discovery and benefit quantification for AI initiatives in R&D contexts (e.g. cycle time reduction in clinical trials, submission timelines, PV processing efficiency); business case development with understanding of R&D cost drivers and regulatory risk dimensions
Life Sciences R&D Data & AI: Data readiness assessment for AI across R&D data types (clinical trial data, ICSR/safety data, regulatory documents, scientific literature); understanding of data standards (CDISC – SDTM/ADaM, MedDRA, WHO Drug); data product concepts and AI adoption support in GxP-compliant environments
Agentic Enterprise (R&D): Identification and scoping of agentic AI opportunities across R&D sub-functions; agent workflow mapping and orchestration design for multi-step R&D processes; human-in-the-loop design with sensitivity to regulatory and patient safety considerations; knowledge of agentic frameworks and platforms (e.g. LangChain, Copilot Studio, AWS Bedrock Agents) and their applicability in Life Sciences contexts
Across all: AI fluency (fundamentals of AI, GenAI, Agentic AI); requirements definition; understanding of Life Sciences regulatory environment (FDA, EMA, ICH guidelines); consulting skills including structured problem-solving, stakeholder management, and ability to engage with both scientific and business audiences

AI prototyping with GenAI and agent builders
Process Mining (Celonis/Signavio/Soroco)
RPA (UiPath/Automation Anywhere)
Low-Code/No-Code development tools (co-pilot studio, N8N, etc.)
SQL and Python basics
Data visualization and storytelling
Understanding of SDLC and Business Analysis
Familiarity with Life Sciences R&D platforms: Veeva Vault (CTMS, RIM, Safety, eTMF), Medidata Rave / Rave RBQM, Oracle Clinical One, Argus Safety, ARIS-g etc.
Knowledge of data standards relevant to Life Sciences: CDISC (SDTM, ADaM), MedDRA, WHO Drug, HL7 FHIR
Prior consulting experience in Life Sciences, or in-industry operating experience in R&D, Clinical Operations, Regulatory Affairs, or Pharmacovigilance

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