Scientist, Systems Immunology

Job Description

AbbVie Immunology is expanding its discovery Systems Immunology team in Cambridge, Massachusetts. The team is focused on identifying new targets for the treatment of autoimmune and chronic inflammatory diseases through computational analysis of human disease tissue-derived datasets and validating targets using humanized experimental approaches.

We are seeking a highly motivated and skilled individual to join our team as a Computational Immunology Scientist. The successful candidate will develop and implement computational approaches to understand disease biology and drug responses using high-throughput molecular profiling data. This role will involve integrating single-cell RNA-seq, scATAC-seq, TCR/BCR repertoire, bulk RNA-seq, and Olink proteomics data to characterize disease-associated cell states, investigate gene regulatory networks, and identify therapeutic opportunities.

The scientist will work in a multidisciplinary team analyzing multiomics datasets to derive insights into immunological diseases and identify novel therapeutic targets relevant to patient cohorts and AbbVie pipeline drugs. The candidate will also contribute to scientific conference presentations, peer-reviewed publications of translational findings, new drug applications, and regulatory filings. If you are passionate about translating complex biological data into actionable insights and advancing personalized medicine, we encourage you to apply.

This position will be based in Cambridge, Massachusetts, and requires passion, attention to detail, prior hands-on experience, and teamwork.

Key Responsibilities:

• Analyze scRNA-seq, and CITE-seq, datasets, including quality control, integration, clustering, and cell-type and cell-state annotation.
• Integrate patient cohorts with reference atlases and perform differential expression, abundance, and pathway analyses, accounting for donor variation and study design.
• Analyze scATAC-seq data to identify differential accessibility, transcription factor motifs, and candidate regulatory programs; integrate accessibility with gene expression to infer peak-to-gene relationships.
• Analyze TCR/BCR repertoires, including clonotype assignment, clonal expansion, diversity, V(D)J usage, and sharing across tissues or time points; link clonotypes to matched single-cell profiles.
• Analyze bulk RNA-seq and Olink proteomics data and integrate multiomics findings to support target prioritization and biomarker hypotheses; maintain documented, version-controlled workflows and evaluate methods using cloud or high-performance computing.
• Collaborate with internal and external scientists to prioritize hypotheses for validation, communicate findings and limitations, and contribute to presentations and publications.

Qualifications

• BS or MS degree in computational biology, bioinformatics, genomics, biostatistics, computer science, or a related quantitative field with typically 5-7+ years (BS) or 2-5+ years (MS) of relevant experience.
• Hands-on scRNA-seq analysis experience or experience with scATAC-seq or TCR/BCR analysis, with interest in developing expertise across both.
• 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.
• Working knowledge of statistical analysis, multiple-testing correction, batch effects, and biological replication in high-dimensional datasets.
• Ability to translate biological questions into analyses, interpret and communicate results critically, collaborate effectively, and manage multiple priorities with attention to detail.
• 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, including AWS and/or SLURM.
• Understanding of immunology and inflammatory disease biology; experience supporting target discovery with experimental teams and communicating research through presentations, publications, or reports.
• Evidence of scientific communication through presentations, posters, publications, technical reports, or related research contributions.

Position will be filled at level commensurate with extent of education, experience, and accomplishment— Scientist I or Scientist II

Additional Information

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: ​

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