Lead Program Manager, Oncology and Multi Specialty Technology Strategy

Position Summary 

The Lead Program Manager, Oncology and Technology Strategy leads the operational delivery and continued maturation of McKesson's Responsible AI Board (RAIB) governance program within the Oncology and Technology Strategy organization. The role translates Responsible AI policy, RAIB Charter requirements, and business priorities into an executable program roadmap, governance structure, operating cadence, service-level expectations, and measurable outcomes. 

Partnering with business and technology leaders, Responsible AI Owners, Legal, Compliance, Privacy, Information Security, Architecture, Data, and other control functions, this role manages the full AI Use Case lifecycle across multiple related governance activities. The Lead Program Manager drives cross-functional alignment, manages program-level risks and dependencies, resolves escalated issues, and maintains visibility into portfolio health, decision quality, control implementation, and ongoing monitoring. 

This is a hands-on program leadership role with accountability for program outcomes rather than only process administration. Success requires the autonomy to lead complex work, influence without direct authority, establish scalable standards, challenge inefficient ways of working, and use analytics and automation to strengthen executive decision-making. 

Key Responsibilities 

Program Leadership and Strategic Alignment 

• Break down Responsible AI program objectives into measurable business and technology outcomes, and maintain a program roadmap that translates governance requirements into executable plans, milestones, deliverables, and success measures. 

• Lead the end-to-end AI Use Case governance lifecycle across intake, triage, RAIB Operations quality review, RAIB and EAIC review, final decision, control implementation, and ongoing monitoring. 

• Partner with program sponsors and senior stakeholders to align governance priorities, secure support, resolve high-visibility issues, and ensure the program continues to deliver enterprise value. 

Governance, Risk, and Dependency Management 

• Establish and maintain the program governance structure, operating cadence, decision rights, escalation paths, service-level objectives, and delivery standards consistent with McKesson's Responsible AI Program, Responsible AI Policy, and RAIB Charter. 

• Identify, consolidate, and manage program-level risks, issues, interdependencies, and resource constraints across governance activities; develop mitigation plans, assign accountable owners, and escalate significant risks with recommended actions. 

• Lead scope, priority, and risk trade-off discussions with stakeholders, driving consensus while preserving policy, control, documentation, and audit requirements. 

• Ensure governance records, risk assessments, approvals, conditions, controls, and notifications are complete, accurate, timely, and audit-ready. 

RAIB and EAIC Leadership 

• Lead preparation and execution of RAIB sessions and, when required, EAIC secondary reviews in partnership with the RAIB Operations and Governance Lead and Sr. Product Portfolio Manager. 

• Ensure review materials enable informed decisions and that every decision, condition, action, owner, and due date is documented accurately in Smartsheet and communicated within the required response window. 

• Serve as the first escalation path for at-risk submissions, unresolved information gaps, missed commitments, or stakeholder conflicts that could affect governance quality, cycle time, or program outcomes. 

Stakeholder Leadership and Cross-Functional Collaboration 

• Act as a trusted program advisor to business and technology stakeholders, translating Responsible AI requirements into clear actions, decision points, and ownership expectations. 

• Build and maintain strategic cross-functional relationships, establish effective feedback loops, and lead regular interactions with sponsors and stakeholders to inform, alert, negotiate, and drive timely resolution. 

• Influence adoption of Responsible AI governance practices without direct authority through clear communication, consultation, education, and disciplined follow-through. 

Performance, Reporting, and Business Value 

• Define, maintain, and report program KPIs and service-level measures, including submission volume, risk-tier trends, cycle time, backlog health, control status, decision timeliness, and stakeholder responsiveness. 

• Develop executive-ready dashboards, scorecards, forecasts, and portfolio health reporting that convert governance data into actionable insights, risks, decisions, and improvement priorities. 

• Work with sponsors to assess program effectiveness, stakeholder satisfaction, and realized business value; recommend adjustments that improve quality, throughput, transparency, and scalability. 

• Use automation, analytics, and AI-enabled insights where appropriate to improve forecasting, identify emerging risks, strengthen prioritization, and reduce manual reporting effort. 

Continuous Improvement and Program Maturity 

• Lead continuous improvement of intake questions, control inventories, risk-tiering methods, review workflows, decision documentation, and monitoring practices. 

• Facilitate lessons learned and retrospective reviews, convert findings into owned actions, and track implementation through measurable improvement. 

• Establish and promote Responsible AI governance standards, reusable tools, and best practices; provide guidance and mentorship to team members and partners supporting the program. 

• Champion a digital-first governance model that embeds analytics, automation, feedback loops, and clear accountability into program operations. 

Required Qualifications and Experience 

• Bachelor's degree in business administration, information technology, computer science, healthcare administration, healthcare informatics, engineering, or a related field, or equivalent experience. 

• Typically 10+ years of progressive experience in project or program management, technology governance, risk and compliance, portfolio delivery, business operations, or a related discipline. Advanced education may be considered in combination with relevant experience. 

• Proven experience leading large, complex, cross-functional programs or multiple related initiatives with critical business value, competing priorities, and substantial consequences of success or failure. 

• Demonstrated success managing program roadmaps, scope, milestones, dependencies, risks, issues, resources, governance forums, and executive reporting with a high degree of autonomy. 

• Experience establishing or maturing governance frameworks, delivery standards, operating models, controls, performance measures, and continuous improvement practices. 

• Demonstrated ability to partner with senior business and technology stakeholders, influence decisions without direct authority, resolve conflict, and drive accountability across organizational boundaries. 

• Experience using data, dashboards, automation, predictive insights, or scenario analysis to improve program visibility, prioritization, risk management, and decision quality. 

• Healthcare, health technology, regulated-industry, Responsible AI, AI governance, privacy, security, compliance, or technology risk experience is strongly preferred. 

Required Skills and Competencies 

• Advanced program management: translates strategic objectives into roadmaps, integrated plans, measurable outcomes, governance structures, and disciplined execution across multiple related initiatives. 

• Program risk and dependency management: anticipates, consolidates, prioritizes, and mitigates complex risks, issues, constraints, and cross-functional dependencies. 

• Stakeholder leadership and influence: builds strategic relationships, adapts communication to the audience, negotiates effectively, and creates alignment among business, technology, legal, compliance, security, privacy, data, and architecture stakeholders. 

• Business and customer orientation: connects governance decisions and delivery priorities to business outcomes, stakeholder value, enterprise objectives, and responsible risk management. 

• Executive communication: prepares concise, decision-ready briefings, dashboards, recommendations, and escalations for senior leaders and governance bodies. 

• Planning and prioritization: self-directs complex work, establishes clarity of roles and decision rights, manages competing priorities, and maintains predictable delivery against commitments. 

• Analytical problem solving: synthesizes information from multiple sources, identifies root causes, evaluates options, and implements practical solutions in ambiguous or evolving situations. 

• Change leadership and continuous improvement: challenges stakeholder mindsets constructively, drives adoption of new practices, embeds feedback loops, and improves program maturity and scalability. 

• Digital fluency: advanced proficiency with Smartsheet, Power BI, Excel, or comparable portfolio, workflow, reporting, and analytics platforms, including building and troubleshooting dashboards and automated reporting. 

• Responsible handling of information: exercises sound judgment, discretion, and rigor when working with confidential, regulated, or risk-sensitive information. 

Measures of Success 

• Responsible AI program milestones, deliverables, decisions, and control activities are completed within established service-level and response-time expectations. 

• Program-level risks, dependencies, resource constraints, and escalations are identified early, assigned clear owners, and resolved through documented mitigation plans. 

• The AI Use Case catalog provides an accurate, current, audit-ready view of all active submissions, decisions, conditions, controls, and monitoring obligations. 

• RAIB and EAIC sessions are decision-ready, and outcomes, actions, owners, and notifications are documented and communicated within required timeframes. 

• Executive dashboards and scorecards are published on schedule and provide actionable visibility into portfolio health, risk trends, cycle time, backlog, SLA performance, and business value. 

• Continuous improvement initiatives produce measurable gains in governance quality, throughput, predictability, stakeholder experience, and scalability. 

• Governance standards, tools, and practices are adopted consistently across the program, with clear accountability and sustained stakeholder engagement. 
   

Working Conditions:

In office requirement, we are Flex and Connect with 2 days a week in office