Who We Are:At Neurocrine Biosciences, we pride ourselves on having a strong, inclusive, and positive culture based on our shared purpose and values. We know what it takes to be great, and we are as passionate about our people as we are about our purpose - to relieve suffering for people with great needs.
What We Do:Neurocrine Biosciencesis a leading neuroscience-focused, biopharmaceutical company with a simple purpose: to relieve suffering for people with great needs. We are dedicated to discovering and developing life-changing treatments for patients with under-addressed neurological, neuroendocrine and neuropsychiatric disorders. The company's diverse portfolio includes FDA-approved treatments for tardive dyskinesia, chorea associated with Huntington's disease, classic congenital adrenal hyperplasia, endometriosis* and uterine fibroids,* as well as a robust pipeline including multiple compounds in mid- to late-phase clinical development across our core therapeutic areas. For three decades, we have applied our unique insight into neuroscience and the interconnections between brain and body systems to treat complex conditions. We relentlessly pursue medicines to ease the burden of debilitating diseases and disorders because you deserve brave science. For more information, visitneurocrine.com, and follow the company onLinkedIn,XandFacebook. (*in collaboration with AbbVie)
About the Role:Leads the development and application of advanced AI/ML methods by using diverse multi-omics datasets to understand mechanisms of drug action and enhance drug discovery and development. This role focuses on building predictive and mechanistic models that directly inform target identification, molecular properties, and patient stratification, to generate actionable insights. This role emphasizes deploying AI/ML into real drug development pipelines, not just exploratory research._
Your Contributions (include, but are not limited to):• Design, develop, and deploy AI/ML models to support key drug development decisions, including target validation, mechanism-of-action modeling, biomarker discovery and patient stratification
- Build predictive and mechanistic models leveraging multi-modal datasets (genomics, transcriptomics, proteomics, scientific knowledge, and clinical data)
- Apply, expand, and validate state-of-the-art AI approaches including deep learning, transformers, and generative models
- Train and fine-tune AI models using internal datasets
- Translate complex biological datasets (e.g., scRNA-seq, spatial omics) into disease-relevant insights and hypotheses. Leverage these datasets to contextualize target gene expression in the context of cell types, disease biology and phenotypes
- Continuously refine processes and workflows to boost efficiency, effectiveness, and reproducibility of AI/ML solutions
- Build and implement AI/ML techniques to build pipelines for antibody design and optimization including structure prediction, sequence modeling and representation, affinity, selectivity, and drug-like properties
- Drive cross-functional initiatives to embed AI/ML into end-to-end drug discovery pipelines
- Stay current with industry trends and apply best practices to improve internal tools and processes
- Develop methods for integrating and visualizing multi-omics data
- Implement quality control measures and standard operating procedures
- Communicate findings to scientific and executive stakeholders, linking AI outputs to program-level decisions
- Stay updated with AI/ML advancements and drive accelerated internal innovations
Requirements:• BS/BA degree in data science or related discipline and 4+ years of related experience in the biotech/pharmaceutical industry OR
- Master's degree in data science or related discipline and 2+ years of related experience in the biotech/pharmaceutical industry OR
- PhD in data science or related discipline
- Strong knowledge and hands-on experience in neuroscience and drug discovery, with a focus on AI/ML and genomic researc
- Advanced proficiency in Python/R and ML frameworks (PyTorch, TensorFlow, etc.)
- Handling large-scale biological datasets in cloud/ high-performance computing environments
- Experience in command line and Linux environments
- Expertise with state-of-the-art AI/ML models such as generative models (diffusion), language models (transformers), multimodal learning, geometric deep learning, graph representation learning
- Deep knowledge of multi-omics integration (proteomics, single-cell, spatial, etc.), as well as disease biology, and molecular mechanisms
- Experience with AI/ML biological transformer models for predicting gene perturbation, molecular efficacy, toxicity, or patient response
- Expertise in bridging the gap between multiple biomarkers and machine learning approaches
- Knowledge of protein structure analysis, antibody biology, and therapeutic antibody design principles
- Ability to develop or customize da