Senior Director, AI and Analytics

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.

McKesson is hiring for a Senior Director of AI and Analytics who will be an integral leader within McKesson's Generics Analytics team. This person will be responsible for developing and implementing analytics-driven strategies that improve the efficiency and effectiveness of pricing decisions made across multiple segments of the organization.

Responsibilities:

• Lead the advanced analytics and data science team responsible for generating insights across segments such as Hospital Systems, Strategic Accounts & National Chains and Independent Pharmacies with focus on Generics business.
• Work with segment business leaders to identify opportunities for data monetization and incremental margin generation and then work with the team to mine the data and generate insights to deliver the business value.
• Drive and lead development of analytical and data science solutions for different Generics business stakeholders
• Utilize advanced analytics, machine learning, and predictive modeling to identify and uncover opportunities for price optimization, margin improvement, revenue acceleration, reducing leakage / revenue loss, product replacement, demand and revenue forecasting, reducing stockouts and optimizing inventory.
• Collaborate with business stakeholder & technology teams to identify existing, or new, areas that can benefit significantly from advanced analytics and data science.
• Communicate strategy and results to technical and non-technical audiences / develop and maintain strong relationships with key stakeholders, partners, and internal clients .
• Streamline AI/ML delivery process with “fail fast” approach for experimenting and Agile implementation to scale the solutions that has been proven to be valuable for the business.
• Accountable for ensuring AI engineering frameworks, ML systems, data pipelines and security with enterprise value priorities and are deployable.
• Have good understanding and appreciation for data governance and AI governance principles and coach the teams to adopt the guidelines for the same.
• Lead building of comprehensive analytical insights engine that optimizes various pricing outputs across multiple algorithms and scenarios to provide the most optimal recommendation and an ability for the users to perform simulations and make best pricing decisions.
• Drive key AI and analytical initiatives, providing clear timelines and actionable plans for implementation and value generation.
• Leverage newer technologies including Gen AI and Agentic AI to deliver faster insights in a more user friendly manner to business users.
• Lead, motivate, and inspire teams to embrace AIML technologies and contribute to the organization's overall success.
• Accountable for aligning with enterprise ML infrastructure decisions and leading ML engineering and operations for Generics Analytics team.
• Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensures all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. 
• Continuously monitor and optimize AI models and algorithms to improve performance and reliability.
• Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
• Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
• Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
• Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.
• Support stakeholders’ analytic needs, gather user requirements, help drive adoption. 
• Manage business stakeholder relationships to drive action and value from data science insights.
• Assist in developing and maintaining long-term stakeholder relationships and networks.

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