Sr. Manager, Commercial AI and Advanced Analytics

About the role

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 analytics programs.
• AI Governance, Compliance & Responsible AI
  • Ensure all AI tools and solutions are explainable, auditable, and compliant with BMS policies and relevant regulatory standards; continuously monitor outputs for bias and incorporate human-in-the-loop mechanisms where required.
  • Champion responsible AI practices — including model documentation, transparency, and lineage from data → model → insight → recommendation — to maintain trust and regulatory readiness.
• Change Management & Adoption
  • Lead structured change management initiatives, feedback loops, and adoption KPI tracking to drive sustained tool adoption and measurable business outcomes across commercial stakeholders.
  • Enable non-technical marketers and business partners to self-serve scenario analyses through intuitive interfaces and comprehensive enablement programs.

Qualifications

• Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, Economics, Operations Research, or a related quantitative field required; PhD in a quantitative field preferred.
• Minimum 3 years of experience in pharmaceutical commercial analytics, decision science, or advanced analytics; prior experience in a US Commercial pharmaceutical Decision Intelligence function preferred.
• 1 years of hands-on Marketing Mix Modeling experience, including Bayesian, Ridge, and hierarchical econometric methods.
• Proficiency with causal inference tools (geo-experiments, matched markets, synthetic controls, uplift modeling) and experience operationalizing them within automated analytics platforms.
• Familiarity with promotional response data, physician-level engagement metrics, media channel measurement (DTC, NPC, digital), and pharmaceutical data ecosystems (claims, APLD, specialty pharmacy).
• Exposure to AI explainability, model risk management, bias monitoring, and human-in-the-loop design patterns.
• Advanced Python programming skills with experience in data science frameworks (Pandas, Polars, PyMC, Scikit-learn).
• Experience with cloud data platforms (Databricks, AWS Redshift) and production data pipelines.
• Proficiency in dashboard and application development (Streamlit, Plotly Dash, React.js).
• Working knowledge of distributed computing, task orchestration (Celery, Redis), Git, Docker, and CI/CD pipelines.
• Experience partnering with Data Governance, Legal, and Privacy teams on responsible AI and compliance frameworks; experience defining SLAs, RACI frameworks, and governance structures preferred.
• Proven track record of reducing analytical cycle times through automation, platformization, and change management; enterprise-scale tool adoption experience preferred.
• Experience presenting to senior business stakeholders and translating complex analytics into strategic recommendations preferred.