Senior Scientist II, Quantitative Bioimaging

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

AbbVie is seeking an independent and innovative Senior Scientist to join our Imaging group. In this role, you will lead the development and integration of advanced microscopy and quantitative imaging strategies, enabling high-resolution, quantitative analysis of biological responses in both 2D and 3D models such as state-of-the-art organ-on-a-chip platforms. This is a high-impact position that directly advances therapeutic model development, supporting key initiatives in Complex In Vitro Models (CIVM) and aligning with the broader objectives of Immunology discovery programs. The work will span the fields of biology, bioengineering, and computational sciences, so candidates with experience combining two or more of these subjects will be preferred.

Key Responsibilities:

• Proficient in imaging assays and data analysis; strong expertise in high content imaging and phenotypic in vitro assay development, multi-parametric computational image analysis, QC, and data visualization.
• Design, implement, and optimize image acquisition pipelines for 2D and 3D cellular/tissue models using different microscopy platforms.
• Promote the use of novel experimental approaches and adoption of complex in vitro models; routinely investigate/create innovative processes, hypotheses and methodologies.
• Collaborate with cross-functional teams including biologists, pathologists, and bioinformaticians to conceptualize assays, execute experiments, and generate robust data.
• Apply advanced machine learning methods to identify novel biomarkers and correlate results with molecular and phenotypic signatures; integrate data from in vitro models and tissue specimens.
• Proactively monitor new technologi