Additionalinformation

Additional Information

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

• The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.
• We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
• This job is eligible to participate in our short-term incentive programs.

Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.

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Companydescription

Company Description

About AbbVie

AbbVie'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 AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn,Facebook, Instagram, X and YouTube.

Jobdescription

Job Description

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.

Qualifications

• 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.