The AbbVie Immunology Discovery Pathology group is at the forefront of AI-driven digital pathology, developing quantitative and translational pathology approaches to support target validation, deepen our understanding of disease biology, and advance drug discovery.

We are seeking an experienced and innovative Image Analysis Scientist with expertise spanning digital pathology, computer vision, machine learning, and scientific programming, combined with a strong foundation in immunology and tissue biology.

Our group generates quantitative insights from H&E and immunolabeled tissue sections and applies emerging spatial transcriptomics and high-plex spatial proteomics technologies to investigate cellular phenotypes, tissue organization, and spatial relationships within the tissue microenvironment.

The successful candidate will independently lead the development and application of advanced computer vision and machine-learning approaches for histopathology image analysis, including deep learning and representation learning. The candidate will also develop robust computational workflows for high-dimensional image-based spatial protein datasets, including approaches to identify and quantify cell populations, characterize cellular phenotypes, and analyze spatial relationships and tissue organization.

This individual will be part of a highly collaborative, multidisciplinary team and will work closely with pathologists, computational biologists, immunologists, and discovery scientists across the organization. The successful candidate will combine computational innovation with biological insight to develop and apply digital pathology approaches that address important scientific questions and help drive innovative drug discovery research.

Responsibilities

• Independently design, develop, optimize, and validate advanced machine learning, deep learning, and computer vision approaches for digital pathology, including cell and tissue segmentation, classification, feature extraction, and representation learning, to identify and quantify histopathologic features, cellular phenotypes, and spatial relationships.
• Develop and deploy robust image analysis workflows for high-dimensional, multiplexed imaging datasets to quantify cell populations and phenotypes and characterize spatial relationships and tissue organization.
• Apply appropriate statistical methods and rigorous model validation strategies to assess model performance and generalizability, including held-out test datasets and quantitative performance metrics such as IoU and F1 score.
• Develop computational workflows to integrate and analyze data across multiple imaging and spatial modalities, including H&E, multiplex immunofluorescence, spatial proteomics, and spatial transcriptomics.
• Develop Python-based tools and scripts to automate image and data-processing workflows and address interoperability challenges across imaging platforms, analysis tools, and data formats.
• Partner closely with pathologists and discovery scientists to perform rigorous quality control, interpret image analysis outputs, and ensure that quantitative results are biologically meaningful.
• Apply strong analytical and problem-solving skills to troubleshoot complex image and data analysis challenges and deliver high-quality results within established timelines.
• Serve as a subject matter expert in computational digital pathology, providing scientific and technical guidance on image analysis strategies, methodologies, and applications across multidisciplinary project teams.
• Stay current with emerging advances in AI, computer vision, and computational pathology, and evaluate and implement new methodologies that can enhance pathology-driven drug discovery.
• Communicate analytical strategies, methodologies, and results clearly and effectively to scientific and cross-functional stakeholders through written and oral presentations.
• Maintain accurate and reproducible documentation of analytical workflows, algorithms, and results in electronic laboratory notebooks and project reports.
• Work independently and collaboratively within multidisciplinary teams to advance project objectives and deliver high-quality scientific results.