Overview In this role you shape enterprise-level strategy and drive long-range planning for a major business unit within a healthcare leader. You will translate business goals into multi-year technical roadmaps, lead AI/ML initiatives at scale, and ensure cross-functional execution in a regulated environment. You'll manage budgets, talent pipelines, and high-stakes partnerships to deliver impactful, compliant AI capabilities that advance McKesson's mission in healthcare. This is a high-visibility role that combines strategic influence with hands-on governance to enable growth and operational excellence.
Compensation / Benefits- Total Rewards
- base pay plus annual bonus or long-term incentive opportunities
- equity considerations
- competitive compensation aligned with market evaluations
- regulatory-compliant compensation practices
Responsibilities- Lead AI/ML engineering organizations through a management layer with people leadership
- Own finance/P&L at scale, including budgets and forecasts
- Provide deep technical credibility in AI/ML systems to guide build/buy/architecture decisions
- Operate in a regulated enterprise environment with healthcare/pharma distribution considerations
- Translate business strategy into multi-year technical roadmap and ensure its execution
Key requirements- 12+ years of professional experience
- 4+ years of management experience
- 5+ years in Responsible AI practices, model risk management, data governance, audit readiness
- Experience with enterprise-scale cloud AI/ML platforms (Azure, Langchain/LangGraph, Python) at production scale
- Strong vendor and partner management and build-vs-buy analysis
- KPI/metrics frameworks and continuous improvement methodologies
- Experience supporting multiple business units or product lines concurrently
- Succession planning and handling significant conflicts and disciplinary matters
- leadership and people management
- cross-functional collaboration
- strategic communication with executives
- AI/ML systems
- ML lifecycle, data architecture, LLM deployment at scale
- Azure services
