Senior Scientist, II Computational Neurobiology
AbbVie
- Location
- Cambridge, MA, United States
- Employment
- Full Time
- Work model
- On-Site
- Level
- Senior
- H-1B history
- 94 approvals (FY2023)
- Posted
- 2h ago
Skills
About this role
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.
Job Description
Overview We are seeking an experienced and highly motivated neuroscientist with strong experimental and quantitative analysis expertise to join our Discovery Psychiatry team. The successful candidate will develop and apply data analysis workflows, including machine learning approaches where appropriate, to identify in vitro functional biomarkers that predict neural circuit modulation and behavioral outcomes in vivo. The ideal candidate will have hands-on experience in electrophysiology and/or other neuronal activity assays, along with the ability to work with multimodal datasets and extract biologically meaningful insights. This interdisciplinary role is an exciting opportunity to help build and standardize cross-scale translational frameworks that support target validation and compound profiling efforts in a dynamic, collaborative drug discovery environment.
Key Responsibilities
Design and execute hiPSC-based neuronal activity assays to support target validation and compound profiling efforts within psychiatry Contribute to the development and implementation of computational analysis workflows that streamline interpretation of complex multimodal datasets Integrate data across assays and scales using quantitative and machine learning-based approaches to generate predictive frameworks that enable identification of translationally relevant functional signatures Ensure alignment of assay development and data analysis efforts with broader cross-functional program team goals Present findings to internal teams to support strategic decision-making in psychiatry drug discovery Provide scientific guidance and mentoring to junior scientists and research associates Qualifications Bachelor’s Degree or equivalent education and typically 12 years of experience, Master’s Degree or equivalent education and typically 10 years of experience, PhD and typically 4 years of experience in neuroscience, computational biology, bioengineering, or a related field Demonstrated experience analyzing complex biological or neurophysiology datasets using quantitative and/or machine learning approaches Strong understanding of data analysis workflows and statistical methods applied to biological data Hands-on experience in the design and execution of in vitro assays related to synaptic and circuit function Experience working with electrophysiology and/or other neuronal functional readouts Strong ability to work in cross-functional teams and effectively communicate scientific findings to both technical and non-technical audiences Desired Qualifications Previous industry experience within early discovery neuroscience Experience building translational frameworks or predictive scoring approaches for functional biology data Familiarity with hiPSC-derived neuronal models Experience working with multimodal datasets and integrating data across experimental platforms 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