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.
Job Summary**
McKesson Corporation is seeking a highly skilled and innovative Lead Data Scientist with experience on Generative AI development. This role will lead the design, development, and deployment of cutting-edge ML & GenAI solutions, leveraging advanced machine learning techniques to solve complex healthcare challenges and drive business transformation.
Job Responsibilities**
- Lead the end-to-end lifecycle of Generative AI and agentic AI solutions , including ideation, research, prototyping, implementation, evaluation, deployment, and production support.
- Architect and develop scalable GenAI systems such as LLM-based applications, Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation workflows to improve decision-making, operational efficiency, and customer outcomes.
- Drive adoption of best practices in prompt engineering, LLM evaluation, fine-tuning strategies, and secure model hosting where applicable.
- Apply advanced data science and machine learning techniques across a wide range of use cases including predictive modeling, forecasting, classification, anomaly detection, recommendation systems, NLP, and GenAI-enabled analytics.
- Develop custom ML models and analytical frameworks tailored to complex healthcare and enterprise data challenges.
- Perform exploratory data analysis (EDA), feature engineering, and statistical analysis to generate actionable insights and guide solution design.
- Collaborate with engineering and platform teams to deploy, monitor, and maintain ML and GenAI solutions in production environments using cloud-native and MLOps best practices.
- Establish model performance tracking, drift detection, reliability monitoring, and continuous improvement processes for deployed models and AI agents.
- Ensure solutions are scalable, cost-efficient, resilient, and aligned with enterprise architecture standards
- Ensure strong model governance , documentation, and auditability across all ML and GenAI solutions.
- Apply Responsible AI principles including explainability, transparency, data privacy, security, and regulatory compliance, particularly within healthcare contexts.
- Provide guidance on safe, compliant, and ethical use of LLMs and agentic AI across enterpris