This role pays less than
77%
of similar roles. Most pay
$169,072–$248,375
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$209k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
252 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 232 roles with salary data.
Sr Scientist, Quantitative Bioimaging will join a cross-functional team to advance quantitative bioimaging capabilities in support of immunology discovery research. The role involves developing and applying computer-vision, machine-learning, and deep-learning approaches to characterize cellular and tissue phenotypes within complex models like organoids, co-cultures, and organ-on-chip systems. You will build robust image-analysis workflows, perform feature extraction from high-content imaging datasets, and integrate results with multimodal data including single-cell or spatial information. Key technical requirements include proficiency in the Python data-science ecosystem, specifically NumPy, pandas, Polars, scikit-learn, and PyTorch. Candidates should possess expertise in image segmentation, representation learning, and foundation models while utilizing tools like CellProfiler or ImageJ/Fiji. This position sits at the intersection of biology, bioengineering, microscopy, and computational science to solve complex problems regarding disease-relevant phenotypes and treatment responses.
Develop and optimize quantitative image-analysis workflows for 2D and 3D cellular and tissue models.
Apply computer vision and machine learning to characterize complex biological phenotypes and disease mechanisms.
Extract multiparametric features from high-content imaging datasets to identify candidate biomarkers.
Integrate imaging data with multimodal datasets including molecular, single-cell, and spatial information.
Establish quality-control frameworks to distinguish biological signals from technical artifacts in image data.
Translate complex biological questions into rigorous analysis strategies for immunology discovery programs.
Communicate methods, results, and limitations effectively to both technical and nontechnical audiences.
What we're looking for
BS, MS, or PhD in engineering, biophysics, computational biology, imaging science, bioinformatics, or a related discipline.
Experience for BS holders, 8-10+ for MS holders, or 0-4+ for PhD holders.
Experience developing quantitative image-analysis or computational solutions including segmentation, feature extraction, and high-content imaging data analysis.
Experience with at least one relevant imaging modality such as confocal microscopy, high-content imaging, or cell painting.
Ability to assess image quality, recognize artifacts, and interpret biologically meaningful phenotypes.
Working knowledge of machine-learning or deep-learning approaches for biological imaging.
Proficiency with the Python data-science ecosystem including NumPy, pandas, Polars, scikit-learn, and/or PyTorch.
Experience with complex in vitro models (preferred); experience with CellProfiler or ImageJ (preferred); exposure to foundation models (preferred).
Python
R
Machine Learning
Genomics
Data Science
Cloud Computing
Large Language Models
Generative AI
Statistics
Computational Biology
Toxicology
Pharmacology
Molecular Profiling
Analytical Workflows
Dashboards