Translational Genomics AI ML Scientist

Syngenta Group•Durham, NC

5d•Hybrid

About The Position

Syngenta Seeds Field Crops is seeking an innovative and highly motivated Translational Genomics AI/ML Scientist based in Durham, North Carolina. The role develops computational capabilities connecting genomics, machine learning, and biological interpretation to crop research and breeding decisions. It transforms large-scale genomic and phenotypic data into scientific insights, predictive models, and reusable digital tools to accelerate trait discovery, improve genomic prediction, and support Seeds R&D. Working across computational biology, AI/ML, bioinformatics, data science, and scientific product development, the scientist will develop methods for representing and learning from biological data, including genomic variation, gene annotations, expression data, pathways, phenotypes, and literature-derived evidence. The role collaborates with scientific, engineering, and product teams to deliver scientifically meaningful, technically robust methods that research users can adopt. It also calls for translating ambiguous research questions into scalable analytical approaches, validated models, and practical decision-support capabilities. Syngenta describes itself as a global agricultural technology company focused on helping farmers meet demand for food and fuel. Its work combines precision breeding, biotechnology trait selection, and digital platforms, with an emphasis on managing agricultural risks and promoting sustainable farming practices.

Requirements

PhD in Bioinformatics, Computational Biology, Genomics, Genetics, Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related discipline.
Strong background in genomics, quantitative genetics, population genetics, or computational biology.
Demonstrated expertise in machine learning and statistical modeling, with proficiency in Python and scientific computing.
Experience working with large-scale biological datasets.
Strong publication or research track record in computational genomics, AI/ML, or a related field.
Excellent communication and collaborative problem-solving skills.
Authorized to work in the U.S. without sponsorship.

Nice To Haves

Experience with genomics and graph-based genomic representations, including GNNs, GATs, graph embeddings, or network analytics.
Experience with foundation models, transformers, DNA language models, or biological large language models.
Experience with knowledge graphs, semantic technologies, biological ontologies, or AI-assisted scientific research workflows.
Experience with cloud computing and production AI environments, including AWS, SnowFlake, distributed computing, or MLOps.
Experience in plant genomics, breeding, crop science, or agricultural biotechnology.

Responsibilities

Lead the scientific design and delivery of AI/ML-enabled genomics capabilities that turn genomic, phenotypic, and biological datasets into insights for trait discovery, genomic prediction, and breeding decisions.
Develop, validate, and operationalize computational methods such as graph machine learning, foundation models, knowledge graphs, and explainable AI, ensuring scientific rigor, reproducibility, and suitability for research and product adoption.
Build scalable bioinformatics and machine-learning workflows integrating genomic variation, annotations, expression data, pathways, phenotypes, and literature-derived evidence across cloud and enterprise data platforms.
Create scalable representations, feature-engineering strategies, and predictive models that capture biological complexity and support research decisions.
Collaborate with geneticists, breeders, molecular biologists, data scientists, software engineers, and product teams to turn biological questions into practical analytical solutions and reusable digital capabilities.
Communicate methods, assumptions, results, limitations, and biological interpretation to technical and non-technical stakeholders.
Identify emerging AI/ML, genomics, and scientific-computing approaches that can accelerate discovery, improve predictive performance, and strengthen digital genomics platforms.
Follow good scientific, data governance, engineering, and reproducibility practices, including documentation, version control, model validation, and transparent handover.

Benefits

Flexible work options.
Full medical, dental, and vision benefit package starting on the first day.
401k plan with company match.
Profit sharing and retirement savings contribution.
Paid vacation and paid holidays.
Maternity and paternity leave.
Education assistance.
Wellness programs.
Corporate discounts.
Professional development and a culture that strives to support work-life balance.