below marketmost similar roles pay hereabove market
This role pays less than
78%
of similar roles. Most pay
$192,500–$266,468
— the blue band above.
At the midpoint, this role pays about
$185k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Bristol Myers Squibb
Bristol Myers Squibb is a global biopharmaceutical company committed to discovering, developing and delivering innovative medicines to patients.
Bristol Myers Squibb currently has
31 open roles
on FindRole.
Listed pay typically runs
$149,295–$180,909
across 22 roles with salary data.
The Associate Director, Commercial AI Product Owner joins the Commercial AI, Analytics & Insights product team to lead the transformation of commercial business processes into machine-readable formats. This role involves owning the unstructured content and relationships within a next-generation knowledge and context layer to make enterprise AI more grounded and accurate. Key responsibilities include translating product visions into technical requirements, designing context layers using graph databases, and implementing ML frameworks to move AI from descriptive to predictive analysis. You will build and maintain golden Q&A evaluation frameworks to monitor performance metrics like accuracy and faithfulness. Required skills include experience with Neo4j, Amazon Neptune, LLMs, RAG, and semantic modeling. You will solve the business problem of making commercial data actionable for smarter, context-aware decision-making across various brands.
Translate the Commercial AI product roadmap into business technical requirements, actionable tasks, and clear milestones.
Convert unstructured business knowledge into a machine-readable context layer using graph databases and semantic models.
Build and maintain a golden Q&A evaluation framework to monitor AI performance for accuracy and groundedness.
Implement ML frameworks to move AI capabilities from descriptive analysis to predictive and prescriptive outputs.
Collaborate with brand teams to develop and continually improve the knowledge graph for specific brand needs.
Partner with Legal and Data Governance teams to ensure AI content meets privacy, explainability, and human-in-the-loop standards.
Track context-layer usage and quality signals to provide measurable performance metrics for the AI product.
What we're looking for
Advanced degree in Computer Science, Software Engineering, Information Science, Artificial Intelligence, Data Science, Life Sciences, or a related quantitative or business field (MS/PhD preferred).
Minimum 5 years of hands-on experience in pharma commercial analytics/decision science or software development.
2 years of experience building or supporting an analytics or AI product or platform.
Hands-on experience with graph databases such as Neo4j and Amazon Neptune.
Hands-on experience designing and maintaining enterprise knowledge graphs, semantic models, ontologies, taxonomies, business vocabulary, and metadata services.
Hands-on experience with AI product development topics including LLMs, RAG, semantic and context layers, prompt or agent configuration, and sub-agent orchestration.
Experience developing end-to-end ML and simulation-type advanced analytics algorithms, such as tree-based models and marketing mix modeling (MMM).
Experience designing and operating AI output evaluation frameworks, including golden Q&A sets, metric-based scoring, LLM-as-judge methods, and regression testing.
Strong foundation in AI governance (risk management, security, privacy, model monitoring, human-in-the-loop) in regulated environments with understanding of HIPAA, GDPR/CCPA, and related standards.
Ability to translate unstructured business and domain knowledge into reusable semantic models that AI can ground on.
Strong stakeholder management and communication skills with the ability to lead without direct authority.
Knowledge of pharmaceutical data, commercial tools, and methods such as Veeva or omnichannel metrics (preferred).
UX research or design experience for analytics or digital products (preferred).
Experience working with offshore or vendor teams and agile tools such as Jira (preferred).
Generative AI
LLM
Agentic Frameworks
LLM Orchestration
Claude
Gemini
Glean
AI Strategy
Roadmap Prioritization
Governance Standards
Data/ML
Information Systems
Statistics
Analytics
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Agent Orchestration
Vector Databases
Model Context Protocol (MCP)
Small Language Models (SLMs)
Model Evaluation
AI Architecture
Human-in-the-loop
Synthetic User Personas
Unit Economics
compute, inference
Token Costs
Data/Analytics
Machine Learning
Generative AI
LLMs
RAG
MLOps
LLMOps
AWS
Azure
GCP
Vector Databases
Model Context Protocol (MCP)
Agentic AI
Prompt Engineering
Fine-tuning
Model Serving
Embeddings
System Design