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

Accenture PLC; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Accenture S&C GN - Industry & Enterprise in Bengaluru seeks a Consultant with 3-7 years of Life Sciences R&D AI experience to drive transformation using AI-enabled solutions across General R&D, Clinical, and Safety & Regulatory workflows.

The role emphasizes identifying AI opportunities, shaping capabilities, data readiness, and process redesign in GxP environments to deliver measurable R&D value.

Qualifications

• AI opportunity identification across Life Sciences R&D functions.
• Knowledge of General R&D, Clinical and Safety & Regulatory data.
• Process discovery and redesign in R&D with GxP
• Functional process knowledge across R&D value chain
• Value discovery and quantification for AI in R&D
• Data readiness assessment for AI in R&D
• Agentic AI opportunities and workflow mapping
• AI fluency and regulatory awareness

Skills

AI Transformation

R&D Domain Expertise

Process Excellence

Functional Transformation

AI Value Architecture

Data & AI

Agentic Enterprise

AI Fluency

Tools

Veeva Vault

Medidata Rave

Argus Safety

Oracle Clinical One

LangChain

Copilot Studio

AWS Bedrock

Job description

Practice: Accenture S&C- GN- Industry & Enterprise
Capability: Life Sciences – R&D (General R&D, Clinical, Safety & Regulatory)
Career Level: Consultant
Experience: 3-7 yrs

WHAT WE ARE LOOKING FOR:

• 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

GOOD TO HAVE EXPERINCE:

• 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

Equal Employment Opportunity Statement

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