Executive Director, CASA, Portfolio & Forecasting AI

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:

The Executive Director, CASA, Portfolio & Forecasting AI is responsible for designing, building, and scaling AI-driven forecasting products and decisioning capabilities that power Bristol Myers Squibb’s portfolio planning, brand forecasting, and financial outlook. This role owns the end-to-end AI product roadmap for demand, inventory, and Gross-to-Net (GTN) forecasting—from data ingestion and model development to deployment, monitoring, and closed-loop learning—ensuring accuracy, transparency, and explainability across brands and markets.

As a senior leader within the AI & Omnichannel organization, this executive partners closely with Finance, Commercial Operations, Market Access, Supply/Inventory, and BI&T to modernize the forecasting tech stack, embed predictive and generative AI where valuable, and institutionalize governance that meets audit, compliance, and risk standards. The role emphasizes portfolio-level forecasting (scenario planning, sensitivity analysis, multi-brand optimization) and launch readiness forecasting.

• Enterprise Forecasting AI Strategy & Roadmap
  • Define and own the multi-year product strategy for AI-enabled forecasting (Demand, Inventory, GTN), with clear business outcomes, KPIs, and adoption milestones.
  • Align the roadmap to enterprise planning cycles, therapeutic area launch timelines, and AI & Omnichannel priorities; maintain a portfolio lens across brands and markets.
  • Quantify value (accuracy lift, cycle time reduction, transparency) and prioritize investments (models, data, tooling, MLOps) accordingly.
• AI Product Ownership:
  Demand, Inventory, and GTN
  • Manage and co‑own AI forecasting products (modules, services, APIs) across Gross Demand, Inventory Management, GTN transformation—including archetypes, features, guardrails, and refresh cadences.
  • Translate business requirements into product backlogs, release plans, and SLAs; ensure explainability and diagnostics are first‑class features.
  • Govern model lifecycle (development, validation, approval, deployment, monitoring, retraining) with BI&T and Finance; establish champion–challenger testing and drift detection.
• Data, Architecture & MLOps Enablement & Collaboration with BI&T Tech Team
  • Partner with BI&T to secure the right pipelines and platforms (cloud data warehouse/lake, semantic layers, feature stores, orchestration) for scalable forecasting AI.
  • Enforce interoperability with planning tools, inventory inputs, and enterprise reporting layers; design APIs for downstream consumption.
• Governance, Compliance & Risk Management
  • Establish forecasting model governance: documentation, sign‑offs, versioning, audit trails, and regulatory/privacy controls for data and outputs.
  • Create business rule frameworks (e.g., demand drivers, GTN rate/mix assumptions, inventory policies) with controlled change management and stakeholder approvals.
  • Run risk assessments for key models/processes; implement mitigation strategies for data gaps, drift, and policy changes.
• Portfolio Planning, Scenarios & Explainability
  • Lead portfolio-level scenario planning (pricing, payer shifts, policy changes, supply constraints) using AI simulations and sensitivity analysis.
  • Deliver explainable AI artifacts (feature importance, stability metrics, back‑testing, variance…