Data Scientist, Consumer Markets

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 looking for a Data Scientist to join the Consumer Markets (eCommerce) Data Science & Analytics team, playing a key role in developing next‑generation, AI‑powered solutions that drive strategic decisions across the organization! This role blends advanced machine learning expertise with strong business acumen to build tools, models, and cloud‑based systems that deliver real business value.

Our ideal candidate is someone currently living in or near Richmond, VA and who thrives on end‑to‑end ownership—from understanding what data is required to making the right modeling decisions to presenting insights in a way that inspires confidence and drives action. If you enjoy building the backend systems that power ML models, innovating with GenAI, and translating complex ideas into clear stories that resonate with business leaders, this role is for you.

• Leverage Consumer Markets’ data infrastructure to develop next‑generation models aligned with strategic priorities.
• Analyze qualitative and quantitative data to develop hypotheses, pilot solutions, and deploy scalable ML models.
• Apply AI/ML techniques to uncover market trends, process inefficiencies, and strategic opportunities.
• Design and implement GenAI and Agentic AI tools to enhance operational workflows.
• Build backend components and cloud‑based pipelines that support machine learning models.
• Develop Python and cloud APIs to enable seamless model deployment in web applications.
• Break down complex problems into scalable work streams and socialize solutions across technical and non-technical teams.
• Collaborate with cross‑functional partners—including Category Management, Marketing, Pricing, Business Development, and Sales—to align on goals and ensure successful adoption of solutions.

• 4+ years of relevant experience.

• Expertise in building backend systems that support machine learning models
  , including data sourcing, preparation, feature engineering, and orchestration.
• Strong understanding of what data is required for model execution and the ability to identify, access, and prepare the right datasets.
• Python expertise (4+ years) including Pandas and modern ML/AI frameworks.
• SQL proficiency (4+ years) with hands‑on experience in Google Big Query
  , Snowflake, and large‑scale data environments.
• Experience with Google Cloud Platform (GCP), Microsoft Azure, and Databricks
  , including pipeline design and model deployment.
• Deep experience with core ML techniques, such as:
  • Statistical tests (t‑tests, Poisson processes)
  • Clustering and segmentation
  • Predictive modeling (e.g., logistic/linear regression)
  • Time series analysis
  • Machine learning methods (Random Forest, SVM, ensemble models)
  • Optimization techniques (e.g., linear programming)
• Experience developing or integrating GenAI and Agentic AI solutions.
• Strong ability to translate complex technical concepts into clear, compelling business insights.

• Bachelor of Science or graduate degree.

• Hybrid role with the majority of work performed from home.
• Team members may be asked to come into the Richmond, VA office (Mayland Drive) for quarterly meetings or company events.
• Position may require up to two trips per year for major business meetings or conferences.

$104,600 - $174,400

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and…