I&P GN - I&E - Life Science Commercial Consultant - Process Excellence-PE

I&P GN - I&E - Life Science Commercial Consultant - Process Excellence-PE

Practice: Accenture S&C- GN- Industry & Enterprise
Capability: Life Sciences – Commercial (Product Management, CRM, Patient Services, Sales & Marketing)
Career Level: Consultant
Experience: 3-7 yrs

WHAT WE ARE LOOKING FOR:

• AI Transformation Advisory: AI opportunity identification and qualification across Life Sciences commercial functions; AI-ready process design and solution shaping for CRM, field sales, HCP engagement, patient services & care management, omnichannel & marketing functions; experience running client workshops and capability assessments in Life Sciences context
• Life Sciences Commercial Domain Expertise (AI fluent): Working knowledge and hands-on experience with implementing AI across one or more Life Sciences commercial sub-functions:
  • CRM & Customer Engagement (field force optimization, intelligent call planning and execution, HCP/HCO master data and alignment, dynamic territory and account planning, closed-loop marketing, regulatory & compliance, key account management)
  • Patient Services (AI-enabled patient onboarding, hub services optimization, streamlined financial assistance (BV, PA, Copay etc.), specialty pharmacy coordination, case management and care coordination, contact center operations, adherence and nurse educator programs)
  • Sales & Marketing (omnichannel orchestration, next-best-action, segmentation and targeting, campaign and content operations, field enablement and launch readiness, incentive compensation, brand and campaign analytics, medical information and inquiry management)
• Process Excellence + AI (Commercial): Process reinvention and design, embedding AI across process discovery & assessment, business process modelling, process mapping, intelligent automation, value realization, and AI governance   
• Platform Transformation: Working knowledge across key commercial Life sciences platforms including, Salesforce Life Sciences Cloud, Agentforce, Health Cloud, Data 360 (formerly Data Cloud); Veeva CRM and Vault CRM, Vault Patient CRM and Vault PromoMats; IQVIA OCE; Salesforce Marketing Cloud, Adobe Experience Cloud etc.
• AI Value Architecture (Commercial): Value discovery and benefit quantification for AI initiatives in commercial contexts including business case development and benefit measurement plans, with understanding of commercial cost drivers, promotional ROI, and compliance risk dimensions
• Life Sciences Commercial Data & AI: Data readiness and quality assessment for AI across commercial data types (HCP/HCO master data; Rx, claims and sales data from IQVIA, Symphony Health and Komodo Health; specialty distribution (867/852) and hub data; consent and channel preference; CRM interaction and call history; content, campaign and patient support case data); understanding of customer and patient 360, master data and identity resolution, interoperability, and knowledge grounding for AI; identifiers, standards and privacy requirements (NPI, DEA, product identifiers, HL7 FHIR, de-identification and tokenization); data product concepts and AI adoption support in regulated, consent- and privacy-governed environments
• Agentic Enterprise (Commercial): Identification and scoping of agentic AI opportunities and working across agent workflow mapping and orchestration design for multi-step commercial processes, including agent roles, tools, knowledge grounding, human approval and escalation paths, evaluation, monitoring and auditability; human-in-the-loop design with sensitivity to promotional compliance, adverse event and product complaint capture obligations; knowledge of agentic frameworks and platforms (e.g. Salesforce Agentforce, Copilot Studio, LangChain, AWS Bedrock Agents)
• AI Delivery & Responsible AI (Commercial): Experience taking at least one AI, GenAI or agentic AI use case through discovery, design, prototype or pilot, testing, rollout or production; working knowledge of prompt and knowledge design, retrieval-augmented generation (RAG), tool and API integration, and model and agent evaluation; application of Responsible AI controls – security, privacy, bias, hallucination and guardrail design, traceability and human oversight – in a regulated commercial environment