Summary

Aging is the biggest risk factor for chronic diseases. The new AI for biology scientist will advance DARe's mission to discover novel transformative therapies for diseases of aging by integrating at scale human pathobiology, experimental science and machine learning. The role will develop, evaluate and apply machine-learning approaches across human genetics, longitudinal EHR cohorts, high resolution multi-omics technologies, cellular imaging and functional data to uncover disease mechanisms and drive therapeutic target identification, prioritization and validation. The successful candidate will combine strong biological insight with expertise in modern machine learning, including representation learning, multimodal methods, biological foundation models and reproducible model-development workflows. Working in a global, multidisciplinary environment, the new team member will partner with biologists, disease experts and data scientists to shape experimental strategies, build scalable analytical workflows and translate complex biomedical data into testable hypotheses and actionable evidence for drug discovery.

Job description

About the Role

Aging is the biggest risk factor for chronic diseases. The new AI for biology scientist will advance DARe's mission to discover novel transformative therapies for diseases of aging by integrating at scale human pathobiology, experimental science and machine learning. The role will develop, evaluate and apply machine-learning approaches across human genetics, longitudinal EHR cohorts, high resolution multi-omics technologies, cellular imaging and functional data to uncover disease mechanisms and drive therapeutic target identification, prioritization and validation. The successful candidate will combine strong biological insight with expertise in modern machine learning, including representation learning, multimodal methods, biological foundation models and reproducible model-development workflows. Working in a global, multidisciplinary environment, the new team member will partner with biologists, disease experts and data scientists to shape experimental strategies, build scalable analytical workflows and translate complex biomedical data into testable hypotheses and actionable evidence for drug discovery.

Key Responsibilities

Partner closely with experimental scientists to understand biological questions and design data-driven approaches to answer them.
Apply expertise in computational biology, human genetics, machine learning and AI approaches to support target identification, prioritization and validation for disease of aging.
Contribute to the design of large-scale experiments and data-generation strategies across genetics, multi-omics, imaging and functional biology.
Integrate diverse biological datasets to uncover disease mechanisms and generate therapeutic hypotheses.
Help establish reproducible and scalable computational workflows and analytical approaches that accelerate discovery across DARe.

Essential Requirements

PhD in Computational Biology, Human Genetics, Bioinformatics, Data Science, Machine Learning, Systems Biology or a related quantitative discipline.
3+ years of experience applying computational and ML approaches to biomedical research, translational science or drug discovery.
Hands on experience with modeling and analyzing diverse biomedical data types including genetic, single cell, perturbation datasets, spatial transcriptomics, and cellular imaging.
Experience evaluating, adapting, and applying biological foundation models to biomedical research, particularly RNA, DNA, single-cell, or spatial data.
Experience with Model deployment (MLops) and agentic frameworks
Strong programming skills in Python and R and proficient in working in modern data-science environments and developing reproducible workflows.
Experience integrating complex biological datasets and translate into actionable insights.
Ability to collaborate effectively with experimental scientists.
Demonstrated curiosity, learning agility and willingness to work across disciplines.

Desirable Requirements

Strong experience in human genetics, including GWAS, rare variant analysis, Mendelian randomization, colocalization or fine mapping.
RWE experience including longitudinal human cohorts, EHRs and disease progression modelling.
Familiarity with biomedical knowledge graphs, representation learning or multimodal learning.
Familiarity with biomedical ontologies, FAIR data principles and data harmonization.
Experience with biological aging clocks.

Requirements

PhD in Computational Biology, Human Genetics, Bioinformatics, Data Science, Machine Learning, Systems Biology or a related quantitative discipline.
3+ years of experience applying computational and ML approaches to biomedical research, translational science or drug discovery.
Hands on experience with modeling and analyzing diverse biomedical data types including genetic, single cell, perturbation datasets, spatial transcriptomics, and cellular imaging.
Experience evaluating, adapting, and applying biological foundation models to biomedical research, particularly RNA, DNA, single-cell, or spatial data.
Experience with Model deployment (MLops) and agentic frameworks
Strong programming skills in Python and R and proficient in working in modern data-science environments and developing reproducible workflows.
Experience integrating complex biological datasets and translate into actionable insights.
Ability to collaborate effectively with experimental scientists.
Demonstrated curiosity, learning agility and willingness to work across disciplines.

Benefits

Health insurance
Life insurance
Disability benefits
401(k) with company contribution and match
Paid time off
Vacation
Personal days
Holidays
Performance-based cash incentive
Annual equity awards

Company overview

Novartis is a global pharmaceutical company headquartered in Basel, Switzerland, dedicated to reimagining medicine to improve and extend people's lives. As an innovative medicines company, Novartis focuses on developing high-value treatments that address society's greatest disease burdens through technology leadership in research and development. The company operates across five core therapeutic areas: cardiovascular, renal and metabolic diseases; immunology; neuroscience; oncology; and hematology. With a workforce of approximately 75,883 employees worldwide, Novartis reaches nearly 300 million patients across 118 countries. The company leverages advanced technology platforms including targeted protein degradation, cell therapy, gene therapy, radioligand therapy, and xRNA to develop breakthrough treatments. Novartis maintains a strong portfolio of oncology drugs and generics while continuously expanding its pipeline through strategic acquisitions and internal research. The company's commitment extends beyond drug development to ensuring global access to medical innovations through novel access approaches and patient assistance programs.