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.

MINIMUM REQUIREMENTS

Typically requires 10+ years of relevant experience

CRITICAL SKILLS

• Experience working in AI/ML-driven environments, supporting rapid experimentation and model deployment (familiarity with Delta Lake, Azure Data Factory is a plus).
• Demonstrated progression to senior levels through recognized expertise and contributions to team and organizational success.
• Extensive expertise in deep learning frameworks (PyTorch, TensorFlow) with demonstrated ability to customize architectures for specific business challenges
• Proven track record of leading complex AI initiatives from conceptualization through production deployment
• Significant experience optimizing and fine-tuning large language models and generative AI systems
• Expert-level Python programming with demonstrated proficiency in software engineering best practices

Specialized Knowledge/Skills –

• Experience developing novel ML methodologies that have demonstrably addressed complex business challenges
• Proven ability to translate cutting-edge research into practical, production-ready systems
• Expert in multiple AI domains (NLP, computer vision, reinforcement learning, time series forecasting)
• Experience mentoring technical teams and elevating organization-wide ML capabilities
• Deep understanding of responsible AI practices including bias mitigation, model explainability, and privacy-preserving techniques
• Strong track record of collaboration with business stakeholders to identify high-impact AI opportunities
• Experience designing and implementing MLOps practices for enterprise-scale model deployment
• Demonstrated ability to balance innovation with practical business outcomes when applying AI techniques

EDUCATION

• Bachelor’s degree required in a quantitative field such as Statistics, Computer Science, Economics, Operations Research, Engineering, Applied Mathematics or related field. MBA or Master’s degree in a quantitative field preferred OR equivalent experience.

Lead AI/ML Engineering for AI Big Bets Rapid Experimentation

• Design and implement advanced ML pipelines enabling rapid model development, experimentation, and iteration
• Develop scalable AI architecture that supports fast prototyping while maintaining production-ready standards
• Create robust MLOps frameworks for continuous integration and deployment of AI models
• Build reusable components for feature engineering, model training, and inference optimization
• Implement monitoring systems to track model performance, drift, and explainability metrics
• Optimize AI systems for computational efficiency and scalability across diverse business applications

Collaborative Leadership & Stakeholder Engagement:

• Partner with data analyst, data scientists, product managers, and business stakeholders to translate business problems into technical AI solutions
• Provide technical guidance on model selection, hyperparameter tuning, and performance optimization
• Lead technical discussions and foster knowledge sharing across multidisciplinary teams

ML System Design & Integration

• Architect end-to-end ML systems that integrate with existing enterprise applications
• Develop standardized approaches for model deployment, A/B testing, and feature flagging
• Design technical solutions for real-time inference and batch prediction workflows
• Create frameworks for automated model retraining and performance validation

Research & Technical Excellence

• Stay current with state-of-the-art AI/ML research and evaluate applicability to business challenges
• Explore and implement novel deep learning architectures and reinforcement learning techniques
• Contribute to internal knowledge repositories and technical documentation