Sr. Data Scientist AI/ML Drug Discovery | Onsite San Diego HQ

Neurocrine Biosciences • San Diego, CA

3d • Onsite

About The Position

Neurocrine Biosciences is a leading neuroscience-focused, biopharmaceutical company dedicated to discovering and developing life-changing treatments for patients with under-addressed neurological, neuroendocrine and neuropsychiatric disorders. This role leads the development and application of advanced AI/ML methods using diverse multi-omics datasets to understand mechanisms of drug action and enhance drug discovery and development. The focus is on building predictive and mechanistic models that inform target identification, molecular properties, and patient stratification, with an emphasis on deploying AI/ML into real drug development pipelines.

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 research.
• 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 data analysis pipelines and algorithms.
• Experienced in quality control measures and standard operating procedures to process sequencing data.
• Familiar with current industry trends and best practices in data science.
• Strong expertise in AI/ML applied to biological, pre-clinical or clinical data, not just general-purpose modeling.
• Demonstrated experience impacting drug discovery or development programs.
• Familiarity in neuroscience or complex disease biology.
• Ability to connect biological questions to data, models‚ and then to actionable decisions.
• Strong understanding of model interpretability and biological validation, not just performance metrics.
• Must understand the underlying science and the "why" behind the data, not just the AI/ML tools.
• Strong communications, problem-solving, analytical thinking skills.
• Detail oriented yet can see broader picture for the department.
• Ability to meet multiple deadlines across a variety of projects/programs, with a high degree of accuracy and efficiency.
• Competence in managing and analyzing complex datasets.

Nice To Haves

• Experience with AI/ML biological transformer models for predicting gene perturbation, molecular efficacy, toxicity, or patient response.
• Knowledge of protein structure analysis, antibody biology, and therapeutic antibody design principles.

Responsibilities

• 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.
• Other duties as assigned.

Benefits

• annual bonus
• equity based long term incentive program
• retirement savings plan (with company match)
• paid vacation, holiday and personal days
• paid caregiver/parental and medical leave
• health benefits to include medical, prescription drug, dental and vision coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree