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Amgen

Makes biotech medicines for serious illnesses

AI-Enabled Knowledge Graphs for Patient Journey Analytics Intern

Data SciencePosted on Nov 13
Internship · Summer 2027Remote in USA$35.34 - $41.20 /hr

About the Job

Requirements

  • Must be available to work a full-time schedule.
  • Must remain enrolled in an accredited college or university following the internship or co-op program.
  • Must be located in the United States for the duration of the internship program.
  • Must be at least 18 years old.
  • Must have graduated with a bachelor’s degree from an accredited college or university.
  • Must be currently enrolled in the relevant graduate program and have completed its first year before the internship starts: MBA for an MBA internship, Master’s for a Master’s internship, PharmD for a PharmD internship, or PhD for a PhD internship.
  • Must be currently enrolled in a Master’s or PhD program in data science, computer science, artificial intelligence, biomedical informatics, biostatistics, epidemiology, computational biology, or a related quantitative discipline.
  • Must have experience using Python, R, or a comparable programming language for data analysis, statistical modeling, machine learning, or AI application development.
  • Must have foundational knowledge of artificial intelligence, graph theory, knowledge graphs, network analysis, graph databases, or graph-based machine learning.
  • Must be able to work with complex datasets and translate scientific or business questions into analytical approaches.
  • Must be able to work independently and collaborate with data scientists, epidemiologists, and other cross-functional partners.
  • Candidates must be authorized to work in the United States for the duration of the program.

Responsibilities

  • Design and develop a prototype knowledge graph representing longitudinal patient journeys, including relationships among patient characteristics, treatments, clinical events, and outcomes.
  • Identify and apply established graph algorithms to investigate epidemiology questions that are challenging to address using traditional tabular data structures and predictive modeling approaches.
  • Explore approaches to stratify patients based on treatment patterns and outcomes and identify patient subgroups with similar clinical journeys.
  • Use AI-assisted tools and methods to support project development, including knowledge graph design, data mapping, code development, documentation, and quality review, with appropriate human oversight, validation, and responsible AI practices.
  • Evaluate additional use cases, including identifying common treatment pathways, characterizing transitions in care, and assessing relationships among treatments, clinical events, and outcomes.
  • Compare knowledge graph-based methods with selected traditional analytical approaches and document their relative strengths, limitations, and appropriate applications.
  • Deliver reusable analytical code, technical documentation, a working prototype or demonstration, and a final presentation summarizing findings, organizational value, and recommendations for future development.

Desired Qualifications

  • PhD or Master’s thesis work focused on graph analytics, knowledge graphs, artificial intelligence, network science, real-world data, or a closely related area.
  • Experience working with healthcare real-world data, such as administrative claims, electronic health records, registries, or other longitudinal patient-level datasets.
  • Familiarity with graph databases, query languages, or analytical frameworks such as Neo4j, Cypher, SPARQL, PyTorch Geometric, or comparable technologies.
  • Experience applying graph algorithms for community detection, clustering, similarity analysis, path analysis, link prediction, or patient stratification.
  • Experience using generative AI, large language models, machine learning, or AI-assisted development tools in an analytical or research workflow.
  • Understanding of epidemiology, patient journey analysis, treatment patterns, clinical outcomes, or real-world evidence research.
  • Experience developing reproducible analytical workflows and communicating technical results to scientific and nontechnical audiences.

About the Company

Amgen

Makes biotech medicines for serious illnesses

Amgen develops and makes biologic medicines, which are drugs built from proteins and antibodies grown in living cells, for serious conditions such as heart disease, cancer, and bone loss. Its anemia drug Epogen, approved in 1989, became one of the biotechnology industry's first blockbuster medicines. Amgen grows these drugs in large tanks called bioreactors, and its products include the cholesterol drug Repatha, the bone-building drug Evenity, and the severe asthma drug Tezspire. It earns money by selling these medicines, mostly through large drug wholesalers that supply hospitals and pharmacies. Amgen was founded in 1980 and is based in Thousand Oaks, California.

Company size
10,001+
Company stage
IPO
Headquarters
Thousand Oaks, CA
Founded
1980

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