Senior Value Engineer - Life Sciences

A Senior Value Engineer specializing in Life Sciences, responsible for designing and implementing AI-driven solutions to optimize supply chain processes, demonstrate value to clients, and support the adoption of Process Intelligence technology within the life sciences sector. Key Responsibilities Understand client AI strategy and supply chain challenges in the Life Sciences sector Translate complex logistics requirements into innovative AI solutions to drive measurable impact Lead technical discovery, capability demonstrations, and guide implementation during the sales cycle Support customers in achieving ROI from AI deployments by enabling autonomous AI agents and process transformation Execute end-to-end Proof-of-Value projects, integrating AI systems with enterprise systems and regulatory frameworks Serve as a technical subject matter expert for Life Sciences supply chain processes and industry standards Requirements At least 5 years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains, including defining AI roadmaps, building compelling ROI TCO business cases, and guiding technical implementations through to value realization. Deep understanding of supply chain business processes native to Life Sciences such as Sales Operations Planning (SOP), Procure-to-Pay, Track Trace, Cold Chain Management, or Quality Control Batch Release, with the ability to translate high-level business needs into specific AI use cases. Expertise in generative AI techniques like Retrieval-Augmented Generation (RAG), few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases such as intelligent chatbots for supplier collaboration or automated extraction of data from complex customs or quality documents. Solid knowledge of Python and common machine learning libraries such as LangChain, pandas, pydantic, scikit-learn, PyTorch, as well as data engineering tools and technologies for handling large, siloed supply chain datasets. Strong presentation skills to both internal and external stakeholders, including supply chain executives and IT leaders, whether leading technical whiteboarding sessions or formal readouts and demos. Bachelor’s Degree in computer science, supply chain management, engineering, mathematics, or related fields, or equivalent work experience. Master’s Degree in computer science, supply chain management, engineering, mathematics, or related fields is preferred. Familiarity with life sciences supply chain data standards and systems such as GS1 EPCIS for traceability, SAP APO IBP, or Kinaxis is preferred. Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other open-source packages is preferred. Experience in deploying and monitoring models at scale across major cloud platforms such as AWS Bedrock, Azure AI, or GCP Vertex is preferred. Compensation/salary range: 131,000 - 154,000 USD Work schedule: Full-time schedule, flexible hybrid work model, unlimited PTO in applicable regions, generous PTO globally Work environment perks: Inclusive parental leave policy (24 weeks paid leave for primary carers, 12 weeks for supporting carers), subsidized Wellhub memberships, mental health counseling, Wellness Weeks, paid volunteer days (Impact Days), community and inclusion events (Inclusion Think Tank, Inclusion Days) Additional benefits: Restricted Stock Units (RSUs), merit-based refresh grants, continuous learning opportunities, mentorship programs, health benefits (health, dental, life, 401k), holistic well-being support, participation in sustainability initiatives, global inclusion programs, mission-driven organization focused on customer value and sustainability #J-18808-Ljbffr