Data Scientist Intern

Our team members are at the heart of everything we do. At Cencora, we are united in our responsibility to create healthier futures, and every person here is essential to us being able to deliver on that purpose. If you want to make a difference at the center of health, come join our innovative company and help us improve the lives of people and animals everywhere.

Cencora is one of the largest drug wholesale and distribution companies in the world. Ranked among the top 10 on the Fortune 500, it serves as a critical link between pharmaceutical manufacturers, healthcare providers, pharmacies, health systems, and patients.

At Cencora, data scientists work on high-impact problems at the intersection of healthcare, supply chain, operations, finance, and strategy. As a Data Scientist Intern, you’ll join a team that values curiosity, rigor, and real-world impact. You’ll receive mentorship and guidance from experienced practitioners while building your skills across statistics, machine learning, data engineering, and business problem-solving – all in a domain where your work contributes to healthier outcomes for customers and patients.

• Explore, clean, and prepare structured datasets from internal and external sources to support analysis and modeling efforts.
• Build, test, and validate statistical and machine learning models under the guidance of senior team members – applying established methods to well-defined business problems.
• Support data pipeline development and maintenance tasks – including data extraction, transformation, and loading.
• Assist with model deployment, testing, and monitoring activities in production environments.
• Create documentation that communicates findings, methodology, and recommendations to technical and business audiences.
• Collaborate with other data scientists, data engineers, analysts, and business stakeholders to contribute to team deliverables and project milestones.
• Follow team standards for code quality, version control, reproducibility, and responsible use of data – including privacy, fairness, and compliance with enterprise governance requirements.
• Participate in team knowledge-sharing sessions and pursue continuous learning to deepen your technical and domain expertise.

• Ability to explain your analyses and results clearly, especially within familiar contexts.
• A learning mindset – genuine curiosity about data science methods, business problems, and the healthcare and supply chain domains.
• Strong attention to detail, follow-through, and a collaborative working style.

• Real-world problem framing – how to take a vague business question and turn it into a well-scoped analytical plan with clear success criteria.
• Production-grade coding practices – including version control workflows, code review norms, and writing clean, reproducible code that teams can build on.
• Healthcare and supply chain domain knowledge – developing the contextual understanding that separates a good analyst from a trusted advisor.
• Stakeholder communication – how to present technical findings to non-technical audiences in ways that are clear, credible, and actionable.
• Modern data science tooling and infrastructure – including cloud-based platforms, large-scale data environments, model deployment pipelines, and the team’s analytics and engineering stack.

• Currently pursuing a bachelor’s degree in data science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field.
• Foundational programming skills in Python and SQL.
• Understanding of core statistical concepts (e.g., hypothesis testing, regression, probability) and introductory machine learning techniques.
• Experience preparing…