Director, AI and Machine Learning for Biology

Novartis•Annandale, NJ

3d•$194,600 - $361,400•Onsite

About The Position

DARe is building an AI-enabled discovery engine that combines longitudinal human data, genetics, multi-omics, imaging, and advanced biological models to identify causal drivers of aging, resilience, and regeneration and translate those insights into therapeutic targets and medicines for age-related diseases. The Director will shape DARe’s global Data Science & AI strategy and lead its US capability in San Diego, bringing strategic leadership and technical depth to high-impact AI programs. The role connects computational, experimental, and translational teams and external partners to improve target discovery and strengthen DARe’s scientific leadership. The Director will set a prioritized portfolio of initiatives, guide the development and application of biological foundation models and other advanced AI methods, and ensure computational approaches inform target decisions, experimental design, and discovery. The role also builds and develops a high-performing US team and promotes collaboration and AI adoption across scientific disciplines, geographies, and internal functions.

Requirements

PhD in Machine Learning, Computational Biology, Data Science, Computer Science, Systems Biology, or a related quantitative discipline, combined with a deep understanding of biology.
10+ years of experience applying advanced computational and AI approaches to biomedical research, drug discovery, or translational medicine.
Proven success leading multidisciplinary global teams across AI, human data, and experimental science.
Demonstrated experience developing or applying advanced machine-learning approaches to complex biological data, such as foundation models, representation learning, generative models, or multimodal learning across sequence, single-cell, spatial, imaging, or other biomedical data.
Strong working knowledge of deep learning, representation learning, multimodal AI, generative AI, and large-scale biological data modeling.
Strong experience in large-scale model training, distributed computation, model adaptation, and deployment within machine-learning operations frameworks.
Strong coding proficiency in Python and deep-learning frameworks, with experience using version-control systems.
Experience with omics data, including transcriptomics, proteomics, and metabolomics, and cellular imaging data.
Ability to lead and influence across matrix organizations, with exceptional communication and stakeholder-management skills.
Demonstrated ability to operate effectively in a fast-paced, highly collaborative research environment.

Nice To Haves

Expertise in statistical genetics, including genome-wide association studies, rare variants, Mendelian randomization, colocalization, or fine-mapping.
Understanding of modern data foundations, including ETL/ELT pipelines, biomedical ontologies, metadata standards, data governance, and FAIR data principles.
Expertise in diseases of aging, neurodegeneration, cardiometabolic disease, regeneration biology, or related therapeutic areas.
Experience with knowledge-graph technologies and scientific knowledge-integration platforms.
Experience developing biological aging clocks.

Responsibilities

Lead the US DARe Data Science & AI efforts and shape the global AI and machine-learning strategy.
Define a prioritized portfolio of Data Science & AI initiatives, establish measures of scientific and operational impact, and ensure investments improve target decisions and experimental design and accelerate discovery.
Co-shape and drive data science and AI efforts supporting target identification, prioritization, and validation across disease programs, contributing to DARe’s long-term scientific AI vision.
Lead the development and application of biological foundation models, multimodal learning, biomedical knowledge graphs, and agentic workflows for evidence integration and target prioritization.
Work across human genetics, longitudinal clinical trajectories, proteomics, transcriptomics, metabolomics, imaging, and electronic health record data.
Build, lead, and develop a high-performing US-based team spanning applied AI, machine learning, computational biology, and data science, while creating growth opportunities for early-career talent.
Evaluate emerging technologies and establish external partnerships to accelerate DARe’s AI capabilities.
Translate scientific questions into computational strategies and select fit-for-purpose statistical, machine-learning, AI, and bioinformatics approaches.
Build organizational connectivity to support collaboration, knowledge sharing, execution, and a one-team culture across geographies and scientific disciplines.
Drive AI adoption across the department.
Partner closely with internal functions, including Data and Digital, Discovery Sciences, and disease areas.