Lead Technical Project Manager

McKesson

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. Lead Technical Project Manager Summary The Lead Technical Project Manager plays a critical role in driving the successful delivery for AI/ML & Advanced Analytics initiatives across the organization. This role bridges business stakeholders, data science teams, and engineering groups to ensure projects are aligned with strategic objectives, well-scoped, and delivered on time. The TPM will manage the end-to-end project lifecycle including intake of requests, requirement gathering, prioritization, roadmap development, milestone tracking, and status communication to leadership.

Requirements

Strong technical acumen in AI/ML and analytics
Excellent project management skills
Ability to communicate effectively with technical and non-technical audiences
Proven ability to align cross-functional teams and remove delivery bottlenecks
Degree or equivalent and typically requires 10+ years of relevant experience.
Less years required if has relevant Master’s or Doctorate qualifications.

Nice To Haves

Experience with Generative AI, LLMOps, AutoML
Familiarity with platforms like Databricks, Vertex AI, Azure ML
Knowledge of governance frameworks and Responsible AI principles

Responsibilities

Program Leadership & Road Mapping – Define and own the end-to-end roadmap for AI/ML initiatives, translating business objectives into actionable programs such as demand forecasting models, NLP-driven chatbots, or predictive analytics. Manage the intake of new requests, gather necessary information for accurate sizing, confirm business value, and ensure alignment with strategic priorities. Evaluate and prioritize initiatives by balancing business value, technical feasibility, resource availability, and associated risks. Use portfolio management tools (Jira, Confluence, MS Project, Smartsheet) to maintain visibility and alignment from ideation through deployment.
Cross-Functional Orchestration – Serve as the central coordinator, orchestrating collaboration across data scientists, ML engineers, data engineers, business analysts, and IT teams. Ensure high-quality pipelines and seamless integration into production systems through APIs, cloud ML services, and CI/CD pipelines.
Execution & Delivery Management – Oversee day-to-day delivery of AI/ML projects using Agile practices (Scrum, SAFe, Kanban). Manage sprints, backlogs, epics, and stories within Jira/Confluence, tracking dependencies and deliverables. Identify risks (e.g., limited data availability, model performance gaps, infrastructure bottlenecks) and implement mitigation strategies. Enforce robust testing standards including unit testing, model validation, A/B testing, and performance benchmarking.
Business Alignment & Stakeholder Management – Act as a trusted partner to business stakeholders and product teams, translating business use cases into clear technical AI/ML requirements. Establish measurable KPIs (accuracy, ROI, adoption rates, productivity impact). Provide regular executive-level updates, communicating technical progress clearly and concisely.
Governance & Compliance – Ensure compliance with regulatory frameworks (HIPAA, GDPR, SOC 2, FDA). Promote Responsible AI principles (fairness, transparency, explainability). Drive documentation, audit readiness, model cataloging, data lineage traceability, and reproducibility. Establish governance for MLOps including model versioning, drift monitoring, and automated retraining.
Metrics & Reporting – Define and track KPIs across technical, business, and delivery dimensions (accuracy, precision/recall, F1 score, ROI, adoption). Provide transparent portfolio health reporting to leadership.
Innovation Enablement – Champion innovation by promoting emerging technologies (Generative AI, LLMOps, AutoML). Collaborate with architecture teams to modernize ML stack (Databricks Lakehouse, Vertex AI, Azure ML). Drive accelerators such as reusable code libraries, pre-trained models, and standardized MLOps pipelines.

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