Career Category
Scientific
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. Amgen is advancing a broad and deep pipeline and portfolio of medicines to treat heart disease, obesity and obesity-related conditions, rare diseases, inflammatory conditions and cancer. 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.
Director, AI and Machine Learning
What you will do
Let’s do this. Let’s change the world. In this vital role you will own an asset-agnostic portfolio that turns human multi-omics, imaging, biomarker, and clinical data into insights supporting clinical drug development and regulatory evidence generation.
You will connect scientific strategy with technical execution by advancing predictive analytics, translation and reverse translation, enabling cross-study learning, and building the infrastructure and ways of working needed for effective adoption of generative and agentic AI. The role combines scientific judgment, technical depth, and people leadership.
Key Responsibilities:
Set the strategy for shared AI and machine learning capabilities
Define and execute the group's AI/ML strategy across therapeutic areas, prioritizing reusable capabilities that address clinical development questions and regulatory evidence needs. Translate scientific priorities into a coordinated roadmap for methods, platforms, and analytical products.
Partner with therapeutic area computational biology leaders and program-facing scientists to integrate shared capabilities into development workflows. Balance innovation, delivery, adoption, resource needs, and operating costs.
Influence Precision Medicine AI/ML strategy
Own the scientific analytics stack and infrastructure roadmap
Lead the architecture and evolution of the group's analytics stack, spanning data preparation and integration, computational workflows, model development, evaluation, deployment, and scientific interpretation in clinical development settings.
Partner with enterprise technology, data engineering, security, and infrastructure teams to deliver scalable AWS-based computing and AI capabilities aligned with enterprise architecture and governance.
Establish reusable software, workflows, and standards for reproducibility, version control, testing, documentation, data lineage, and ongoing platform and model operations.
Advance clinical and translational analytics
Lead the development and application of statistical, machine learning, deep learning, and other AI methods to integrate multi-omics, imaging, biomarker, and clinical data from human studies.
Develop reusable approaches for patient stratification, response prediction, pharmacodynamic analysis, and mechanism-of-action studies, linking biomarker and imaging findings to clinical endpoints, efficacy, and safety.
Direct cross-study and cross-asset analyses that support translation and reverse translation, using biological hypotheses to inform clinical biomarker strategies and clinical observations to generate mechanistic hypotheses for experimental testing and future studies.
Anchor analytical strategies in clinical trial design, endpoints, patient populations, assay limitations, confounding, missing data, and uncertainty.
Apply validation appropriate to the scientific question, clinical decision, and intended evidentiary use.
Guide…