Senior Scientist, Systems Immunology, Machine Learning

Company Description

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 immunology, oncology, neuroscience, and eye care and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us atwww.abbvie.com. Follow @abbvie onX,Facebook,Instagram,YouTube,LinkedInandTik Tok.

Job Description

AbbVie Immunology is expanding its Immunology Discovery team in Cambridge, Massachusetts. We at Immunology Discovery are focused on identifying targets for the treatment of autoimmune and chronic inflammatory diseasesusing an interdisciplinary approach where we use high-throughput single cell biology experiments and cutting-edge computational approaches to study human disease derived samples and datasets.

We are seeking a highly skilled Senior Scientist with a strong background in machine learning and computational biology to lead efforts computational modeling of cellular response. This role is pivotal in understanding large disease specific single cell datasets and predicting disease relevant targets. You will leverage our existing immune cell atlas and build predictive models that enhance our ability to identify new therapeutic targets.

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 internal and external peer reviewed journals. If you are passionate about translating complex biological data into actionable insights and contributing to the advancement of medicine, we encourage you to apply.

This position will be based in Cambridge, Massachusetts (hybrid optional) and requires passion, attention todetail, prior hands-on experience, and teamwork.

Key Responsibilities:

1. Computational modeling of cellular response at single cell level:

• Lead the development of computational models to predict how cells respond to perturbations using cutting-edge machine learning algorithms.

2. Perturbation Analysis:

• Apply machine learning methods to analyze drug responses, leveraging experimental data from both single-cell and bulk transcriptomics.

3. Integration of Multi-Omics Data:

• Work on integrating various types of omics data (e.g., RNA-seq, epigenomics, proteomics).

4. Method Development and Optimization:

• Develop and benchmark novel methodologies to ensure robust in silico analyses.

• Optimize existing machine learning pipelines for efficiency, reproducibility, and scalability across multiple datasets.

5. Data Interpretation and Clinical Integration:

• Interpret results to inform biomarker identification and therapeutic target discovery.

Qualifications

Qualifications:

• BS, MS, or PhD degree in Computational Biology, Bioinformatics, Computer Science, or related field with typically 10-12+ (BS), 8-10+ (MS), or 0-4+ (PhD) years of experience.

• Proven experience in applying machine learning algorithms to biological datasets, particularly single-cell RNA-seq.

• Strong background in multi-omics data integration and perturbation-based analysis.

• Experience with computational frameworks like PyTorch, TensorFlow, or similar platforms for model training and validation.

• Excellent problem-solving skills, with the ability to work on complex datasets and extract meaningful insights.

• Strong publication record demonstrating expertise in predictive modeling and drug response analysis.

• Collaborative mindset with the ability to work across interdisciplinary teams.

Preferred Qualifications:

• Experience in immune system biology, with a focus on immune cell atlas development or immune response modeling.

• Familiarity with spatial transcriptomics, CRISPR perturbation data, and their integration into predictive models.

• Experience in building and optimizing computational pipelines for large-scale single-cell RNA-seq analysis.

The position will be filled at a level that aligns with the candidate's education, experience, and achievements. Senior Scientist I or Senior Scientist II.

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 ingood faith it will pay for this role at the time of this posting based on the job grade for this position. Individualcompensation paid within this range will depend on many factors including geographic location, and we may ultimatelypay 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/visioninsurance and 401(k) to eligible employees.

• This job is eligible to participate in our short-term incentive programs.

Note: No amount of payis considered to bewages 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 benefitsthat are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid andmay be modified at the Companys sole and absolute discretion, consistent with applicable law.

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives, serving our community and embracing diversity and inclusion. It is AbbVies policy to employ qualified persons of the greatest ability without discrimination against any employee or applicant for employment because of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information, gender identity or expression, sexual orientation, marital status, status as a protected veteran, or any other legally protected group status.

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Location: Cambridge, MA, US

Posted Date: 11/16/2024