Executive Director, CASA, Portfolio & Forecasting AI

Position Summary

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 explanations) for executive and Finance stakeholders.
  • Institutionalize closed‑loop learning—link forecasts, actuals, and adjustments to improve models and business rules over time.
• Stakeholder Partnership & Adoption
  • Serve as the single point of accountability for forecasting AI with Finance, Commercial Operations, Market Access, Supply/Inventory, BI&A, BI&T, and TA leaders.
  • Drive adoption via training, playbooks, office hours, and executive readouts; publish recurring forecast quality dashboards and action‑oriented commentary.
• Team Leadership & Vendor Ecosystem
  • Build and lead a high‑performing forecasting AI cross‑matrix team (product managers, data scientists, ML engineers, model risk) across HQ and offshore; set a culture of rigor, transparency, and impact.
  • Manage vendors/partners (data sources, planning platforms, model ops tools); enforce SLAs and value realization.