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

Amgen•Thousand Oaks, CA

About The Position

Amgen is seeking a graduate intern to help design and integrate tools that accelerate safety assessments from early discovery through post-marketing. The intern will build practical workflows that ingest molecular omics datasets, clinical adverse event data, and internal preclinical model data to surface mechanistic insights, score and weight potential safety concerns, and support nonclinical safety strategy. The role works closely with toxicologists, clinical safety scientists, computational biologists, and data engineering partners. The internship offers experience spanning problem scoping, data ingestion, evaluation, validation, and communication. The intern will contribute tools that inform target safety assessments and decision-making across Amgen programs, gain exposure to translational safety science, mechanistic biology, real-world datasets, and multi-omics and literature evidence, and receive guidance from TSRS scientists, data scientists, and engineers. The intern must be available for a full-time schedule, remain enrolled at an accredited college or university following the internship, and be located in the United States for the duration of the program. Candidates must be authorized to work in the United States for the program's duration; sponsorship for future full-time roles is not guaranteed. The opportunity is for Summer 2027. The stated U.S. base pay range is $35.34–$41.20 per hour.

Requirements

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 an accredited MBA, master's, PharmD, or Ph.D. program and have completed 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 continue enrollment at an accredited college or university after the internship program 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.
Preferred degree concentration in toxicology, computational biology, bioinformatics, computer or data science, life sciences, or a related field.
Preferred completion of at least one year of graduate study by the internship start.
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; familiarity with NLP/LLMs, including prompt engineering, retrieval-augmented generation, or summarization.
Experience with omics datasets and 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.

Nice To Haves

Familiarity with APIs or simple workflow orchestration.
Basic cloud literacy, including AWS.
Experience building dashboards or internal utilities, such as Shiny.

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 to support risk-based decision-making.
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 to improve tools and analyses.
Collaborate with TSRS team members to prioritize high-impact use cases.