McKesson

Compensation

Full Description

Job Description: • Collaborate with data scientists, machine learning engineers, and analytics teams to provide technical direction for AI and advanced analytics platforms • Work closely with data warehousing, data engineering, and cloud platform teams to design optimal architectures for AI-driven data solutions • Enable the scalable use of AI-generated outputs (e.g., ML predictions, extracted signals, model outputs) in conjunction with structured data to support analytics and oncology insights • Partner with senior management and stakeholders to communicate AI system capabilities, implementation approaches, assumptions, and limitations in clear, non-technical language • Participate in the full lifecycle of AI and data platform solutions, including planning, design, implementation, deployment, monitoring, and ongoing maintenance • Design, build, and maintain production-grade AI pipelines, shared frameworks, and supporting services in the cloud (e.g., AWS, GCP, Azure; Azure preferred) • Design, test, and maintain AI-enabled applications and services using modern software engineering and testing methodologies • Perform code reviews and help define engineering and AI code standards to ensure high-quality, scalable, and maintainable solutions • Develop and maintain scalable data and AI pipelines using Python and supporting technologies • Design and implement data architectures that support downstream analytics and access by McKesson analysts and AI data consumers • Drive innovation and develop reusable engineering solutions to support AI workloads, model execution, inference pipelines, and integration into downstream data products • Evaluate new AI-related tools, frameworks, and platforms to improve scalability, reliability, and developer productivity prior to broader adoption Requirements: • Degree or equivalent and typically requires 7+ years of relevant experience • 3+ years of relevant experience in data engineering or software development roles supporting analytics or AI-enabled solutions • Proficiency in Python and SQL • Demonstrated experience developing and maintaining reliable, production-grade data pipelines and analytical datasets • Experience building and supporting internal tools or applications used for data validation, monitoring, review, or operational analytics workflows • Working knowledge of application integration patterns, including service-based architectures and data access layers that support UI-driven tools • Hands-on experience using Databricks for data processing, analytics development, and collaboration with data science or analytics teams • Experience working within Microsoft Azure environments, applying standard engineering practices to deliver maintainable, well-documented solutions Benefits: • competitive compensation package • annual bonus or long-term incentive opportunities