Senior Scientist, Computational Pathology and AI Informatics

Career Category

Research

Job Description

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.

Senior Scientist, Computational Pathology and AI Informatics

What you will do

Let’s do this. Let’s change the world. We are seeking a Senior Scientist to join the Computational Imaging and Digital Biomarkers team. This hands-on role will develop and apply computational approaches to digital pathology and spatial biology data, with a strong focus on extracting biologically interpretable insights from tissue. You will integrate tissue morphology, cell phenotypes, spatial relationships, and molecular measurements to characterize the tumor microenvironment, investigate mechanisms of disease and drug response, and support biomarker discovery.

The role will also evaluate and apply H&E foundation models as reusable representations for image analysis and downstream biological questions. Working closely with pathologists, translational scientists, and computational biologists, you will translate research questions into rigorous, reproducible analyses and communicate what the results mean biologically.

Key Responsibilities

• •Design and apply spatial analysis approaches to characterize tissue organization, cellular composition and states, spatial neighborhoods, cell-cell interactions, distances, colocalization, and regional heterogeneity.
• •Work with pathologists and disease-area scientists to translate biological questions into analysis plans, select appropriate spatial statistics, and distinguish robust findings from technical or sampling artifacts.
• •Develop and apply computational pathology workflows for digitized H&E whole slide images, including tissue detection, tiling, quality control, feature extraction, segmentation, classification, and slide- or region-level modeling.
• •Evaluate and adapt H&E foundation models and learned representations for downstream tasks such as morphology characterization, clustering, prediction, and cross-modal association.
• •Apply approaches such as feature extraction, fine-tuning, weakly supervised learning, and multiple instance learning to adapt H&E foundation models to scientific questions and available datasets.
• •Assess model generalizability, interpretability, failure modes, and the relationship between model outputs and relevant biological or pathological features.
• •Design and execute analyses of spatially resolved tissue data, including multiplex immunofluorescence, high-plex imaging, and spatial transcriptomic or proteomic datasets.
• •Integrate spatial measurements with H&E morphology, IHC, clinical, molecular, and other biomarker data to generate biologically grounded, testable insights.
• •Develop visualizations and concise scientific narratives that connect computational results to tissue biology, mechanism of action, biomarker hypotheses, and program decisions.
• •Establish fit-for-purpose quality control, benchmarking, and validation approaches for spatial analyses.
• •Compare computational outputs with pathologist review, orthogonal assays, or other reference data, and communicate the strength and limitations of the evidence.
• •Build reproducible Python and R-based workflows for preprocessing, analysis, visualization, and reporting of whole slide and spatial datasets.
• •Apply computer vision, machine learning, image analysis, and spatial statistics methods that are rigorous, interpretable, scalable, and fit for purpose.
• •Contribute to study design, data and analytical strategy, algorithm selection, evaluation criteria, and documentation for research and translational projects.
• •Collaborate in a matrixed, global environment and deliver high-quality analyses aligned with project objectives and timelines.
• •Present methods, findings, assumptions, and limitations to technical and non-technical audiences; contribute to publications, presentations, internal knowledge sharing, and external scientific collaborations.
• •Provide technical guidance to colleagues and share reusable analysis practices, code, and documentation across the team.

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a leader with these qualifications.

Basic Qualifications

Doctorate degree

OR

Master’s degree and 3 years of computational biology, bioinformatics, biomedical engineering, computer science, applied mathematics, life sciences experience.

OR

Bachelor’s degree and 5 years of computational biology, bioinformatics, biomedical engineering, computer science, applied mathematics, life sciences experience.

Preferred Qualifications

• •Demonstrated experience analyzing biomedical imaging data, with experience in digital pathology, microscopy, or spatial biology.
• •Experience applying quantitative methods to spatially resolved tissue data, such as multiplex imaging or spatial transcriptomics, and interpreting results in a biological or translational context.
• •Proficiency in Python and experience with scientific computing and data analysis libraries (NumPy, SciPy, scikit-learn, OpenCV); ability to work with image, cell-level, and spatial data.
• •Working knowledge of machine learning and image analysis concepts, including segmentation, classification, co-registration, feature extraction, and model evaluation.
• •Experience working with H&E foundation models or other pretrained vision models for pathology image analysis, including evaluating model representations or adapting them for downstream tasks.
• •Ability to communicate analytical results, assumptions, and limitations clearly and collaborate effectively across scientific and technical disciplines.
• •Experience with multiplex immunofluorescence, spatial transcriptomics, or related spatial molecular assays, including data quality assessment and interpretation.
• •Experience analyzing whole slide images and image-derived cell or tissue features.
• •Proficiency with using AWS or a similar cloud computing platform for machine learning and high-throughput computational workflows.
• •Proficiency in R and experience with performing statistical analyses using imaging-derived, clinical, and multi-omics datasets.
• •Experience benchmarking computational methods across cohorts, assays, tissue regions, or spatial scales, and communicating the implications for biological use.
• •Peer-reviewed publications, presentations, or other evidence of scientific contributions in computational imaging, spatial biology, pathology, or biomarker research.
• •Familiarity with version control, documentation and software development best practices.

What you can expect from us

As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.

The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.

In addition to the base