Senior Scientist II, Informatics

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

We seek a talented and highly motivated Data Scientist with experience in image analysis to join our expanding Spatialomics team within the Discovery Immunology Pathology group. In this role, you will develop and apply computational methods to image-based spatial transcriptomics and proteomics datasets, including the integration and interpretation of multi-modal omics data in order to derive biological insights into disease pathogenesis and identify novel therapeutic targets. The successful candidate will be part of a unique multidisciplinary team working closely with pathologists, image analysts, and research scientists across the organization in a highly dynamic and collaborative environment to support pathology driven spatial omics projects that contribute to innovative drug discovery research.

This position is an onsite role located in Worcester, MA.

Key Responsibilities:

• Design, develop and implement computational pipelines for processing and analyzing spatial omics datasets, including spatial transcriptomics and proteomics (mIF, IMC).
• Collaborate with pathologists to develop methods to integrate and analyze spatial transcriptomics data with other spatial modalities, such as IHC or mIF
• Perform image analysis tasks for spatial transcriptomics and proteomics datasets, including de-array and alignment for multi-omics integration, cell segmentation and phenotyping
• Collaborate with pathologists to validate computational tools for spatial data analysis in the context of tissue structure to ensure accuracy and reproducibility
• Ensure scalability and efficiency of the pipelines to handle large and complex datasets
• Lead the development of user-friendly data