Grad Intern - Data Science (TS&BA)

Career Category

Job Description

Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Grad Intern - Data Science (TS&BA)

What You Will Do

Let’s do this. Let’s change the world. This internship provides an opportunity to apply your data science expertise to real-world toxicology challenges within the drug development process. You will work alongside experienced toxicologists and data scientists to analyze complex biological data, develop predictive models, and support the safety assessment of novel drug candidates.

Key Responsibilities

• Data Analysis & Modeling: Analyze large-scale toxicology datasets using advanced statistical techniques, machine learning algorithms, and computational models to assess drug safety.
• Predictive Toxicology: Develop and apply predictive models to forecast adverse drug reactions and toxicological outcomes, leveraging multi-omics data (genomics, proteomics, etc.), high-throughput screening results, and clinical data.
• Data Integration & Visualization: Collaborate on projects that involve the integration of diverse data sources, including experimental, clinical, and real-world evidence data, and create visualizations that provide meaningful insights into toxicological risks.
• Collaborative Research: Work cross-functionally to explore the application of data science in discovery and regulatory toxicology and contribute to decision-making in drug safety evaluations.
• Innovative Approaches: Explore and apply cutting-edge methodologies such as artificial intelligence (AI) and machine learning (ML) to identify support predictive toxicology efforts.
• Reporting & Documentation: Document and present findings through technical reports, scientific presentations, and potential publication in peer-reviewed journals.
• Drug Development Exposure: Gain insights into the end-to-end drug development process, from early discovery through to clinical trials, focusing on how toxicology impacts decision-making in regulatory submissions and safety assessments.

Learning Opportunities

• Exposure to the …