Scientist I - Computational Biology, Systems Immunology

AbbVie Immunology is expanding its early discovery Systems Immunology team in Cambridge, Massachusetts. The team is focused on identifying and evaluating new targets for the treatment of autoimmune and chronic inflammatory diseases through computational analysis of human disease tissue-derived datasets and validation using humanized experimental approaches.
We are seeking a highly motivated and skilled individual to join our team as a Computational Biology Scientist. The successful candidate will develop and apply computational approaches across single-cell, bulk transcriptomic, proteomic, and other multi-omics datasets to generate insights into disease biology, identify and prioritize therapeutic targets, and support translational research.
The individual will work in a multidisciplinary team analyzing human disease datasets, including single-cell RNA-seq, CITE-seq, bulk RNA-seq, Olink proteomics, and other molecular profiling data. The candidate will collaborate with disease-area and experimental teams to translate complex biological data into actionable target and biomarker hypotheses, and will contribute to scientific presentations, internal reports, and publications.
This position will be based in Cambridge, Massachusetts, with a hybrid work arrangement, and requires scientific curiosity, attention to detail, hands-on computational experience, and a collaborative work style.
Key Responsibilities:
Single-Cell and Tissue Atlas Analysis
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.
Bulk RNA-seq and Olink Proteomics
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.
Machine Learning and Quantitative Analysis
Apply statistical and machine-learning methods to high-dimensional biological datasets and evaluate model performance using fit-for-purpose validation strategies.
Use cloud and high-performance computing environments to support scalable analysis of large datasets.
Scientific Communication and Collaboration
Interpret findings and communicate scientific data, analytical limitations, and recommendations clearly to multidisciplinary internal stakeholders.
Present work to disease-area experts and collaborate closely with computational, translational, and experimental scientists.
Provide computational expertise for internal and external collaborations and contribute to conference presentations, publications, and other scientific deliverables, as appropriate.
BS or MS degree in computational biology, bioinformatics, genomics, biostatistics, computer science, or a related quantitative field, with typically 2–4 years (MS) or 5–6 years (BS) of relevant research or industry experience.
Hands-on experience processing and analyzing single-cell RNA-seq data.
Experience analyzing bulk RNA-seq data, including differential expression and functional enrichment.
Proficiency in Python and/or R and experience working in Linux-based computing environments.
Experience with commonly used bioinformatics tools and frameworks such as Scanpy, AnnData, Cell Ranger, scvi-tools, DESeq2, GSEA, WGCNA, or related methods.
Experience applying statistical or machine-learning approaches to high-dimensional biological data.
Ability to translate biological questions into analytical plans, critically interpret results, and communicate conclusions and limitations.
Demonstrated ability to work collaboratively, manage multiple priorities, and deliver high-quality analyses with attention to detail.
Preferred Qualifications
Experience analyzing 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.

Employment type
Full-time

Work arrangement
No