Computational Immunology Scientist — Multi-Omics & scRNA-seq

AbbVie Inc.; Cambridge (MA); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Paid time off

Short-term incentives

Equal opportunity employer

Job summary

AbbVie Cambridge, MA, is seeking a Computational Biology Scientist to develop and apply computational approaches across single-cell, bulk transcriptomic, proteomic, and other multi-omics datasets to generate insights and prioritize targets. You will collaborate with disease-area and experimental teams to translate data into actionable hypotheses and contribute to internal reports and publications.

The role requires hands-on computational experience, attention to detail, and a collaborative style

Qualifications

• Experience with Olink or other high-plex proteomics datasets.
• Experience with CITE-seq, ATAC-seq, spatial transcriptomics, or integrative multi-omics analysis.
• Experience building or benchmarking machine-learning models, including convolutional neural networks, autoencoders, generative models, transfer learning, or foundation models.
• Experience with cloud or high-performance computing environments, including AWS and/or SLURM.
• Familiarity with gene regulatory networks, co-expression analysis, pathway enrichment, and target-prioritization workflows.
• Understanding of immunology and autoimmune or chronic inflammatory disease biology.
• Experience collaborating with experimental scientists or disease-area teams to support target discovery or validation.
• Evidence of scientific communication through presentations, posters, publications, technical reports, or related research contributions.

Responsibilities

• Establish and apply analysis strategies for single-cell RNA-seq and CITE-seq datasets, including quality control, integration, clustering, cell-type and cell-state annotation, and differential expression.
• Build, curate, and analyze large single-cell atlases and internal patient cohorts derived from human disease tissues.
• Benchmark integration and representation-learning methods, including variational autoencoders, generative models, and foundation-model approaches.
• Use single-cell data to identify disease-associated cell states, pathways, biomarkers, and context-specific target biology.
• Develop and execute analysis workflows for bulk RNA-seq data, including quality control, normalization, differential expression, pathway enrichment, and cross-cohort comparison.
• Analyze Olink proteomics datasets to identify disease-associated proteins, biomarkers, pharmacodynamic signals, and target-related molecular signatures.
• Integrate bulk RNA-seq and Olink results with single-cell and other molecular datasets to connect tissue-level signals with relevant cell types and biological pathways.
• Collaborate with experimental and translational scientists to interpret transcriptomic and proteomic findings and define follow-up analyses.

Tools

AWS

SLURM

Job description

AbbVie Cambridge, MA, is seeking a Computational Biology Scientist to develop and apply computational approaches across single-cell, bulk transcriptomic, proteomic, and other multi-omics datasets to generate insights and prioritize targets. You will collaborate with disease-area and experimental teams to translate data into actionable hypotheses and contribute to internal reports and publications.

The role requires hands-on computational experience, attention to detail, and a collaborative style