Grad Intern – Target Safety Risk Weighting and Evaluation Strategies (Summer 2027)

Amgen•Thousand Oaks, CA

8d•$35 - $41

About The Position

Amgen’s Summer 2027 graduate internship focuses on designing and integrating tools that accelerate safety assessments from early discovery through post-marketing. The intern will build practical workflows that use molecular -omics datasets, clinical adverse event data, and internal pre-clinical model data to surface mechanistic insights, assess potential safety concerns, and support non-clinical safety strategy. The work involves close collaboration with toxicologists, clinical safety scientists, computational biologists, and data engineering partners. The internship offers experience across problem scoping, data ingestion, evaluation, validation, and communication. The intern will contribute tools that inform target safety assessments and decision-making, gain exposure to translational safety science and real-world datasets, and receive guidance from TSRS scientists, data scientists, and engineers.

Requirements

Must be 18 years or older.
Must have graduated with a bachelor’s degree from an accredited college or university.
Must be currently enrolled in an accredited MBA, Master’s, PharmD, or Ph.D. program and complete the first year of that program before the internship starts, as applicable to the internship type.
Must be available to work a full-time schedule.
Must remain enrolled in an accredited college or university following the internship or co-op.
Must be located in the United States for the duration of the internship program.
Must be authorized to work in the United States for the duration of the program.

Nice To Haves

Degree concentration in Toxicology, Computational Biology, Bioinformatics, Computer/Data Science, Life Sciences, or a related field.
At least one year of graduate study completed by the start of the internship.
Proficiency in Python and R for data analysis and modeling, including tools such as pandas, NumPy, scikit-learn, tidyverse, or Bioconductor.
Proficiency with AI coding tools such as Codex or Claude.
Experience with Jupyter or R Markdown, SQL, Git/GitHub, and environment management.
Coursework or hands-on projects in AI/ML, and familiarity with NLP/LLMs, including prompt engineering, retrieval-augmented generation, or summarization.
Experience with -omics datasets, biological knowledge bases or ontologies such as GO or MedDRA, and public biology resources such as Open Targets or similar.
Comfort integrating heterogeneous data, such as preclinical, clinical, and literature data, and mapping it to pathways or phenotypes.
Familiarity with target safety assessment, off-target mechanisms, and translational safety principles.
Familiarity with APIs or simple workflow orchestration; basic AWS cloud literacy is a plus.
Experience building dashboards or internal utilities, such as Shiny, is a plus.

Responsibilities

Use and refine AI tools for data aggregation, processing, interpretation, and automated report generation across safety workflows.
Integrate public and internal multimodal data, including omics, preclinical findings, clinical safety signals, and real-world evidence, into risk-scoring and weighting methods.
Generate mechanistic off-target hypotheses and pathway-level safety rationales to support internal safety evaluation strategies.
Document methods, assumptions, and limitations, and contribute to the Target Safety Scoring Assessment codebase.
Present progress and findings to TSRS stakeholders and incorporate feedback into tools and analyses.
Collaborate with TSRS team members to prioritize high-impact use cases.