Senior Manager, AI Implementations

BeiGene

2d•$137,400 - $182,400

About The Position

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 workstreams more efficient.

Requirements

Bachelor's degree in AI, Data Science, Computer Science, Engineering, Statistics, Biomedical Informatics, Life Sciences, or a related quantitative/technical field; Master's degree preferred.
BS Degree with 7+ years of experience across data products, AI/ML, GenAI implementation, data engineering, BI, or commercial/medical technology in enterprise environments.
3+ years of recent oncology experience focused on patient journeys, patient-level data, customer/HCP engagement, commercial domain, medical domain, and related domain knowledge.
Strong understanding of pharmaceutical/biotech commercial and medical data ecosystems, including patient journey data, field insights, CRM/field activity, medical/commercial knowledge assets, omnichannel engagement, and BI/reporting layers.
Experience building or governing semantic layers, data catalogs, business glossaries, metadata frameworks, ontologies, knowledge graphs, or metrics layers for AI use.
Hands-on experience with GenAI/RAG concepts, including document ingestion, embeddings, vector databases, retrieval strategies, prompt engineering, grounding, evaluation, monitoring, and guardrails.
Coding proficiency in Python and SQL; ability to prototype independently and partner effectively with engineering teams.
Experience with data warehouse, lakehouse, and cloud platforms such as Snowflake, Databricks, AWS, Azure, or GCP; experience with BI tools such as Power BI, Tableau, Qlik, or similar preferred.
Familiarity with LLMOps/MLOps, APIs, workflow automation, data pipelines, and unstructured data processing.
Understanding of data privacy, security, compliance, and responsible AI practices in healthcare/life sciences environments.
Ability to translate ambiguous business needs into practical AI/data products and technical requirements.
Strong cross-functional collaboration with Commercial, Medical, field teams, data science, engineering, BI, compliance, privacy, legal, procurement, corporate affairs, clinical, and vendor partners.
Executive-ready communication and storytelling skills; ability to explain AI outputs, data limitations, and recommended actions to technical and non-technical audiences.
Demonstrated ability to drive adoption, standardization, and change management across matrixed teams.
Builder mindset; comfortable testing new tools, writing code, learning quickly, and moving from proof-of-concept to scalable capability.
Strong project/product management skills, including prioritization, requirements definition, delivery coordination, and value measurement.

Nice To Haves

Advanced degree in AI, data science, biomedical informatics, life sciences, computer science, statistics, engineering, or related field.
Experience with oncology product launch, patient-level claims/EHR/EMR data, data tokenization/identity resolution, MDM, or customer 360 capabilities.
Experience with automated deck generation, document intelligence, copilot/assistant experiences, or agentic workflows.
Experience designing AI evaluation frameworks, prompt libraries, guardrail testing, and monitoring dashboards.
Experience in commercial and medical data governance or AI governance committees.

Responsibilities

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, consistent, and fit for use in business and medical workflows.
Enhance existing RAG and GenAI models to generate meeting-ready decks, briefs, summaries, talking points, and insight narratives based on unstructured commercial and medical content.
Define reusable templates and quality checks for AI-generated presentation materials, including audience, purpose, key message, evidence, caveats, next-best-action, and source attribution.
Partner with field, brand, medical, and other commercial teams to connect unstructured content with structured data and BI outputs so meeting materials are timely and decision ready.
Drive adoption of automation that reduces manual preparation time while maintaining quality, compliance, and stakeholder trust.
Ensure end-to-end customer and patient journey solutions leverage AI capabilities in the intended manner, grounded in BeOne's data landscape and aligned to priority use cases.
Map AI use cases to upstream data sources, semantic definitions, model workflows, BI outputs, CRM/field workflows, and user actions.
Partner with Commercial and Medical teams to validate that AI-generated insights are reflected accurately and timely in dashboards, reporting layers, and performance-management routines.
Bring oncology patient journey expertise to ensure AI-driven insights are clinically and commercially meaningful, with appropriate granularity and limitations clearly understood.
Write code and build prototypes, scripts, prompts, evaluation harnesses, APIs, or workflow automations to test and accelerate new AI capabilities.
Explore emerging AI platforms, data products, and vendor capabilities; recommend practical fit-for-purpose solutions and help transition successful experiments into scalable delivery.
Engage with Corporate Affairs, Procurement, Clinical, and other enterprise teams to identify and support AI solutions that improve efficiency in high-value tasks.
Produce documentation, training, demos, and user guidance that help teams adopt AI responsibly and effectively.
Support roadmap planning, backlog prioritization, vendor coordination, delivery milestones, and measurement of value/impact.
Apply AI governance, model validation/evaluation, data privacy, information security, and regulatory expectations, including HIPAA, GDPR, GxP where applicable.
Document model assumptions, source data, prompts, retrieval configurations, known limitations, review controls, and audit trails.
Partner with compliance, legal, privacy, and security teams to ensure patient-level data handling, role-based access controls, and promotional/medical content boundaries are respected.
Implement monitoring and feedback mechanisms for accuracy, data quality, fairness, hallucination risk, user experience, and operational performance.