Senior Manager, AI Implementations

BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer.

The Global Commercial and Medical Affairs Technology team at BeOne is seeking a Senior Manager, AI implementation to help operationalize AI capabilities across Commercial and Medical teams. This role sits at the intersection of oncology data, customer and patient journey data, GenAI implementation, and enterprise data platforms.

This role will help define the semantic layer over BeOne's commercial and medical data warehouse, ensuring structured and unstructured data assets are described consistently so AI models, RAG experiences, insight engines, and BI layers can leverage the right data with the right context.

The Senior Manager will coordinate with Commercial, Medical, Data Engineering, Data Science, Compliance, Privacy, and field teams to ensure free‑text insight collection, RAG chat interfaces, AI‑generated outputs, and customer journey solutions are implemented with appropriate guardrails, traceability, and business relevance.

The ideal candidate brings 3-5 years of recent oncology experience focused on patient journeys and patient‑level data, strong familiarity with commercial and medical data ecosystems, and a hands‑on builder mindset with the ability to write code, explore emerging platforms, and turn AI concepts into practical capabilities that make cross‑functional work streams more efficient.

• Lead creation and ongoing governance of a semantic layer on top of the commercial and medical data warehouse, including entities, attributes, definitions, metrics, business rules, relationships, lineage, and data quality expectations.
• Partner with data engineering, BI, MDM, and business stakeholders to define how patient, HCP, account, field insight, engagement, journey, patient‑level, and medical/commercial data assets should be represented for AI use.
• Translate data definitions and business context into reusable metadata, ontologies, knowledge objects, and retrieval patterns that improve AI model grounding.
• Support the design of an insight generation engine on top of the data and semantic layers, ensuring outputs are accurate, explainable, timely, and available through the appropriate BI and workflow layers.
• Define evaluation criteria for data readiness, retrieval quality, insight accuracy, confidence, freshness, source attribution, and business actionability.

• Coordinate across Commercial, Medical, field, compliance, privacy, legal, and technology teams to ensure AI guardrails for free‑text inputs and field‑collected insights are implemented correctly.
• Establish practical operating procedures for acceptable use, input validation, sensitive data handling, prompt and response controls, human review, and exception management.
• Monitor adoption and quality of field insight capture workflows, identifying risks related to incomplete context, inconsistent terminology, duplication, bias, hallucination, or inappropriate data use.
• Ensure AI outputs appropriately reflect approved data sources, business rules, medical/legal/regulatory considerations, and commercial/medical operating boundaries.

• Validate and continuously improve RAG‑based chat interfaces across Commercial and Medical teams, including source coverage, retrieval performance, answer quality, citation/traceability, feedback loops, and escalation paths.
• Work with teams to ensure data and unstructured knowledge assets are collected, curated, permissions‑aware, and available for use in chat, summarization, insight, and assistant experiences.
• Partner with data science and engineering teams to refine embeddings, chunking strategies, metadata filters, prompt templates, evaluation datasets, and LLMOps/monitoring capabilities.
• Design and run testing processes to confirm RAG outputs are accurate, grounded,…