Lead Data Scientist – Commercial Intelligence & ML Operations

McKesson•Richmond, VA

1d•$155,300 - $258,800

About The Position

McKesson is seeking a Lead Data Scientist specializing in Pricing Intelligence and Machine Learning Operations. The role leads development and implementation of advanced analytical models and machine-learning solutions to optimize pricing strategies and ensure models are deployed robustly, scalably, and efficiently in operational environments. It leads analytics across customer, product, pricing, and commercial domains; supports optimization, forecasting, recommendation, and AI-driven decisioning capabilities; and translates analytical findings into business recommendations. The role also partners with engineering teams to operationalize solutions and provides technical leadership and mentorship to other data scientists. The position is with McKesson Medical-Surgical (MMS), a McKesson Corporation subsidiary and publicly reported segment that distributes medical-surgical supplies, pharmaceuticals, diagnostic equipment and supplies, and related solutions to healthcare providers outside traditional hospitals. MMS serves alternate and non-acute care markets across the United States. The stated base pay range is $155,300–$258,800. Additional compensation, such as an annual bonus or long-term incentive opportunities, may be offered. McKesson describes its compensation package as part of its Total Rewards and says compensation depends on factors including performance, experience, skills, equity, market evaluations, and geography.

Requirements

At least 7 years of experience in Data Science, Machine Learning, Advanced Analytics, or a related quantitative field.
Strong Python and SQL skills.
Strong foundation in statistical modeling, machine learning, regression, clustering, and predictive analytics.
Experience deploying and supporting models in production environments.
Experience working with large-scale datasets and cloud-based analytical platforms.
Strong understanding of data pipelines, model inputs and outputs, and data-quality validation.
Experience with model monitoring, lifecycle management, and explainability.
Strong problem-solving and communication skills.
Demonstrated ability to lead technical work and collaborate across business and technology teams.
Bachelor’s degree in a quantitative or technical field, with substantial relevant experience.

Nice To Haves

Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Economics, Engineering, or another quantitative discipline is preferred.
PhD in a quantitative discipline.
Experience in pricing, revenue management, forecasting, optimization, or commercial analytics.
Experience with recommendation systems, segmentation, or predictive modeling.
Experience with MLOps and production model deployment.
Familiarity with Generative AI and modern AI-assisted analytical solutions.
Experience supporting business-critical analytical applications.
Prior experience mentoring or leading other data scientists.
Experience in the healthcare or pharmaceutical industry.

Responsibilities

Develop, maintain, and improve predictive and machine-learning models.
Lead advanced analytics initiatives across customer, product, pricing, and commercial domains.
Monitor production models for accuracy, reliability, and data quality.
Troubleshoot model and data issues across analytical workflows.
Establish model validation, testing, monitoring, and governance standards.
Translate analytical findings into clear business recommendations.
Partner with engineering teams to operationalize models and analytical solutions.
Provide technical leadership and mentorship to other data scientists.
Support development of optimization, forecasting, recommendation, and AI-driven decisioning capabilities.