Sr Scientist, Quantitative Bioimaging

Company Description

About Abb Vie

Abb Vie'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 including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about Abb Vie, please visit us at  Follow @abbvie on Linked In, ()  Facebook, Instagram () , X ()  and You Tube.

()

Job Description

We are seeking a Senior Scientist to advance quantitative bioimaging capabilities in support of immunology discovery research. In this role, you will work within a cross-functional team with access to multiple microscopy modalities, complex in vitro models, multimodal biological datasets, and advanced computational infrastructure. You will develop and apply quantitative imaging and machine-learning approaches to characterize cellular and tissue phenotypes, support the development of complex in vitro models, and generate insights that advance immunology discovery programs.

The role sits at the intersection of biology, bioengineering, microscopy, and computational science; candidates who have combined expertise in two or more of these areas are especially encouraged to apply. This position is onsite in Worcester, Massachusetts.

Key Responsibilities

+ Develop, optimize, and apply imaging assays and quantitative image-analysis workflows for 2D and 3D cellular and tissue models across multiple microscopy platforms.

+ Collaborate with wet-lab assay scientists, imaging specialists, and computational scientists from experimental design through image acquisition, analysis, and data delivery.

+ Design and implement reproducible computer-vision and machine-learning pipelines for image segmentation, object detection, feature extraction, phenotypic profiling, and representation learning.

+ Extract biologically meaningful, multiparametric features from high-content imaging datasets and use them to characterize disease-relevant phenotypes, mechanisms, and treatment responses.

+ Establish practical quality-control and evaluation frameworks that assess image and assay quality, distinguish biological signal from technical artifacts, and define appropriate limitations and acceptance criteria.

+ Apply statistical, machine-learning, and deep-learning methods to identify candidate imaging biomarkers and relate imaging phenotypes to molecular, functional, and other biological readouts.

+ Maintain clear experimental and analytical records; analyze, visualize, and present results rigorously to both technical and nontechnical audiences.

+ Stay current with advances in quantitative bioimaging, image analysis, computer vision, and foundation models for biological imaging, and evaluate opportunities to incorporate relevant approaches into team workflows.

Qualifications

Basic Qualifications

+ BS, MS, or PhD in engineering, biophysics, computational biology, imaging science or a related discipline, with typically 10-12+ (BS), 8-10+ (MS), or 0-4+ (PhD) years experience.

+ Demonstrated experience with microscopy, high-content imaging, quantitative image analysis, or phenotypic in vitro assay development.

+ Experience working with at least one relevant imaging modality or assay type, such as confocal, high-content imaging, or cell painting. Candidates should be able to assess image quality, recognize common artifacts, and interpret biologically meaningful phenotypes.

+ Working knowledge of machine-learning or deep-learning approaches for biological imaging, with the ability to understand model behavior, assess performance, and contribute to appropriate evaluation strategies.

+ Proficiency with the Python data-science ecosystem, including Num Py, pandas, Polars, scikit-learn, and/or PyTorch.

+ Strong biological grounding, attention to experimental rigor, and demonstrated ability to collaborate effectively with wet-lab and computational colleagues.

+ Excellent written, verbal, and presentation skills, including the ability to explain methods, results, assumptions, and limitations to diverse audiences.

Preferred Qualifications

+ Familiarity with complex in vitro models, such as organoids, organ-on-chip models and other advanced in vitro model platforms.

+ Familiarity with image-based profiling, dimensionality-reduction methods, clustering, morphological feature analysis, or phenotypic similarity analysis.

+ Peer-reviewed…