below marketmost similar roles pay hereabove market
This role pays less than
71%
of similar roles. Most pay
$158,212–$221,000
— the blue band above.
At the midpoint, this role pays about
$159k
versus about
$190k
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
145 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 140 roles with salary data.
JOB TITLE: Senior Scientist II, Informatics – Computational Digital Pathology & Image Analysis
The Senior Scientist II, Informatics – Computational Digital Pathology & Image Analysis joins the Immunology Discovery Pathology group to advance drug discovery through AI-driven pathology. This role involves independently leading the development of machine learning, deep learning, and computer vision approaches for histopathology image analysis. You will build robust computational workflows to quantify cell populations, characterize phenotypes, and analyze spatial relationships within tissue microenvironments using H&E, multiplex immunofluorescence, and spatial proteomics. Key responsibilities include developing Python-based tools using NumPy, pandas, SciPy, scikit-learn, OpenCV, PyTorch, and TensorFlow. You will integrate high-dimensional spatial transcriptomics and proteomics data to solve complex biological questions. The role requires expertise in image segmentation, representation learning, and statistical validation to provide biologically meaningful insights into disease biology and target validation.
Design, develop, and validate machine learning and computer vision models for cell and tissue segmentation and classification.
Develop robust image analysis workflows to quantify cell populations and characterize spatial relationships in multiplexed imaging datasets.
Integrate and analyze data across multiple modalities including H&E, multiplex immunofluorescence, spatial proteomics, and spatial transcriptomics.
Create Python-based tools and scripts to automate image processing workflows and ensure interoperability across different platforms.
Apply statistical methods and rigorous validation strategies to assess model performance using metrics like IoU and F1 score.
Partner with pathologists to perform quality control and ensure that quantitative image analysis results are biologically meaningful.
Serve as a subject matter expert to provide technical guidance on computational pathology strategies to multidisciplinary project teams.
Maintain accurate and reproducible documentation of all analytical workflows, algorithms, and results in electronic laboratory notebooks.
What we're looking for
PhD in computational biology, biomedical engineering, computer science, AI, machine learning, computer vision, biomedical image analysis, or a related quantitative discipline.
4+ years of relevant applied experience.
Strong foundation in mathematics, statistics, image processing, computer vision, and machine learning to develop computational methods for quantitative image analysis.
Demonstrated expertise in developing, optimizing, and validating machine learning and image analysis approaches for H&E, immunohistochemistry, and multiplexed tissue imaging.
Strong proficiency in Python for scientific computing and machine learning using libraries such as NumPy, pandas, SciPy, scikit-learn, OpenCV, PyTorch, or TensorFlow.
Experience analyzing high-dimensional multiplexed imaging datasets, including multiplex immunofluorescence or imaging mass cytometry, for cell segmentation and spatial analysis.
Experience with digital pathology platforms like Visiopharm and QuPath, including developing customized computational approaches.
Strong background in immunology and tissue biology, or experience applying computational image analysis to immunologic diseases (preferred).
Python
PyTorch
Hugging Face
Lightning
MONAI
OpenCV
scikit-image
Computer Vision
Deep Learning
Machine Learning
Image Segmentation
Foundation Models
Representation Learning
Linux
High-Performance Computing
Version Control
Cellpose
StarDist
InstanSeg
OME-Zarr
DINO
CLIP
SAM
Python
PyTorch
MONAI
SimpleITK
ITK
PyRadiomics
nnU-Net
3D Slicer
OpenCV
Deep Learning
MLOps
DICOM
Radiomics
Cloud ML
Data Science
Data Visualization
Data Analysis