Senior Scientist, Systems Immunology, Machine Learning

AbbVie - Cambridge, MA

Full-time - Senior

Cambridge, MA

Chemical Manufacturing

About the position

The Senior Scientist in Systems Immunology at AbbVie will lead computational modeling efforts to understand cellular responses in the context of autoimmune and chronic inflammatory diseases. This role involves analyzing large single-cell datasets and integrating multi-omics data to identify novel therapeutic targets. The position requires a strong background in machine learning and computational biology, with a focus on translating complex biological data into actionable insights for drug development.

Responsibilities

• Lead the development of computational models to predict cellular responses using machine learning algorithms.
• Apply machine learning methods to analyze drug responses from single-cell and bulk transcriptomics data.
• Integrate various types of omics data, including RNA-seq, epigenomics, and proteomics.
• Develop and benchmark novel methodologies for robust in silico analyses.
• Optimize existing machine learning pipelines for efficiency and scalability across datasets.
• Interpret results to inform biomarker identification and therapeutic target discovery.

Requirements

• BS, MS, or PhD in Computational Biology, Bioinformatics, Computer Science, or related field with 10-12+ years (BS), 8-10+ years (MS), or 0-4+ years (PhD) of experience.
• Proven experience applying machine learning algorithms to biological datasets, especially single-cell RNA-seq.
• Strong background in multi-omics data integration and perturbation-based analysis.
• Experience with computational frameworks like PyTorch or TensorFlow for model training and validation.
• Excellent problem-solving skills with the ability to extract insights from complex datasets.
• Strong publication record in predictive modeling and drug response analysis.
• Collaborative mindset to work across interdisciplinary teams.

Nice-to-haves

• Experience in immune system biology, focusing on immune cell atlas development or immune response modeling.
• Familiarity with spatial transcriptomics and CRISPR perturbation data integration into predictive models.
• Experience in building and optimizing computational pipelines for large-scale single-cell RNA-seq analysis.

Benefits

• Paid time off (vacation, holidays, sick)
• Medical, dental, and vision insurance
• 401(k) plan
• Participation in short-term incentive programs