Sr. Manager, Commercial AI and Advanced Analytics

Working with Us
Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.

Summary:

The Sr. Manager, US Commercial AI & Advanced Analytics is a hands-on technical leader and platform engineer who drives the design, development, and deployment of AI-powered commercial analytics platforms — including Agentic AI Marketing Mix Models, RAG-enabled knowledge systems, and interactive decision-support applications — to accelerate data-driven investment, media, and sales force optimization decisions across the US pharmaceutical brand portfolio.

This role combines advanced data science with commercial strategy, translating cutting-edge modeling into scalable, governed, and responsibly deployed tools that deliver measurable business impact. This role will help shape how BMS builds the next generation of always-on, agent-enabled measurement capabilities at scale.

Responsibilities:

AI/ML Development & Marketing Mix Modeling

• Lead the Agentic AI Marketing Mix Modeling initiative, providing strategic guidance on establishing the Analytical Ready Data (ARD) foundational layer for downstream agentic workflows
• Evaluate and integrate Bayesian modeling techniques into the Marketing Mix framework, including informed priors, MAP estimates, and parallel MCMC chain orchestration to optimize model stability and predictive accuracy
• Build and maintain RAG pipelines — integrating Brand Guidelines, KPI knowledge bases, and Azure OpenAI LLMs via APIs — to enable contextual knowledge retrieval and AI-driven narrative insights across structured and unstructured marketing data
• Implement model validation frameworks, back-testing routines, calibration checks, and sensitivity analyses to ensure model reliability and fitness for use before deployment
• Architect, deploy, and orchestrate autonomous and semi-autonomous analytics agents within the Agentic platform — including multi-agent coordination, task sequencing, and role/function definition — enabling progression from descriptive analytics through causal analysis, root-cause insights, and predictive recommendations

Data Engineering, Platforms & Visualization

• Collaborate with engineering teams to identify key data sources, define business rules, and validate data schemas on Databricks, ensuring data governance and accessibility
• Build scalable ETL data pipelines connecting enterprise data warehouses and flat files, optimizing ingestion and analytical efficiency
• Develop, maintain, and enhance production-grade analytics applications — including interactive dashboards (Streamlit, Dash) and guided chatbot/scenario simulation interfaces (React.js, Python) — supporting Marketing Mix Modeling, promotional tracking, and spend/ROI forecasting
• Engage brand and commercial stakeholders when technical model questions arise — explaining modeling assumptions, uncertainty, and sensitivity findings in accessible, business-relevant terms

Cross-Functional Collaboration & Data Partnerships

• Partner with BI&T, Data Science, TA Analytics, and Engineering to deliver analytics-ready datasets, feature stores, and semantic layers, standardizing and accelerating insight generation across commercial functions
• Partner with Brand Commercialization & Operations teams to deliver on-demand data analyses supporting investment and sales force optimization decisions
• Champion automation and platformization to reduce manual effort and external vendor reliance — identifying and implementing reuse opportunities across the enterprise analytics ecosystem
• Coordinate with Data Governance, Legal, and Privacy teams to define SLAs, RACI matrices, and privacy-by-design principles for cross-functional ana