Senior Scientist I

Job Description

AbbVie Immunology is expanding its early discovery (The early Transformational and Translational Immunology Discovery/TTID-Systems Immunology) team in Cambridge, Massachusetts. TTID 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 through 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 play a crucial role in developing and implementing cutting-edge computational approaches to predict drug responses using high-throughput molecular profiling data. Specifically, this role will involve integrating multi-omics data, building gene regulatory network models, and leveraging machine learning techniques to assess drug combination opportunities.

The person will work in a multi-disciplinary team analyzing multi-omics datasets to derive insights into immunological diseases, identify novel therapeutic targets specific to patient cohorts and AbbVie pipeline drugs. In addition, the candidate will contribute to presentations at scientific conferences and publications of translational findings in peer reviewed journals, new drug applications and regulatory filings. If you are passionate about translating complex biological data into actionable insights and contributing to the advancement of personalized medicine, we encourage you to apply.

Key Responsibilities

Single Cell Analysis Strategies:Establish and develop novel analysis strategies for single-cell RNA-seq, TCR-seq, and BCR-seq data.

Leverage single-cell data to identify context-specific regulatory networks associated with disease targets and biomarkers.

Multi-Omics Integration:Integrate diverse omics data (e.g., gene expression, proteomics, genomics) to infer regulatory networks.

Collaborate with cross-functional teams to identify potential drug targets and optimize therapeutic strategies.

Machine Learning Model Development: Develop machine learning methods to assess target prioritization.

Utilize internal omics data to predict drug responses and optimize treatment outcomes, and perform perturbation analysis

Scientific Communication:Interpret findings and effectively communicate scientific data, concepts, and recommendations to internal stakeholders.

Participate in feature design, code reviews, and technical discussions.

External Collaborations:Provide expertise and technical consultation for external collaborations and partnerships with academia and industry.

Contribute to collaborative projects that address critical gaps in drug response prediction.