Sr. Manager, Commercial AI and Advanced Analytics

Job description

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 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 Analytics & Modeling 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) AI/ML & Agentic AI Experience launching or scaling agentic AI solutions, including LLM integration, RAG frameworks, multi-agent orchestration, and NLU/NLP pipelines Exposure to AI explainability, model risk management, bias monitoring, and human-in-the-loop design patterns Engineering & Technical Stack 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 Governance & Stakeholder Skills 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 Key Competencies Strategic thinking — translating business challenges into analytical frameworks with measurable impact Strong cross-functional collaboration across brand, engineering, BI&T, governance, legal, and leadership teams Excellent communication and data storytelling for both technical and non-technical audiences Deep commitment to responsible AI: explainability, fairness, auditability, and ethical standards High accountability with the ability to manage multiple priorities and iterate