Breeding Data Scientist at Syngenta Group

Durham, North Carolina, United States -

Full Time

Start Date

Immediate

Expiry Date

27 May, 26

Salary

0.0

Posted On

26 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Experimental Design, Statistical Modeling, Machine Learning, Simulation, R, Python, SQL, Cloud Computing, Genomic Datasets, Phenomic Datasets, Environmental Datasets, Predictive Modeling, Stakeholder Management, Cross-functional Teamwork, Version Control

Industry

Farming

Description

Company Description About Syngenta At Syngenta Seeds Field Crops, we're shaping the future of agriculture and empowering farmers to meet the ever-growing demand for food and fuel. We’re a global Ag Tech powerhouse, headquartered in the United States, with passionate, local experts collaborating with farmers to deliver solutions that create market opportunities. We unite precision breeding, advanced biotechnology trait choice, and digital platforms for unmatched in-field performance. Our seeds help mitigate risks such as disease, insect, weed, and extreme weather pressures, all while promoting sustainable farming practices that protect and enhance our planet. Join our mission of revolutionizing food security and transforming agriculture. Job Description At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta's Analytics & Data Sciences Team is seeking a Breeding Data Scientist in Durham, NC. This role will collaborate with a multidisciplinary team of biostatisticians and phenomics and enviromics scientists within the Germplasm Development Team to enhance breeding strategies through improved experimental design, analysis, and decision-making capabilities. The ideal candidate will help deliver innovations that accelerate genetic gain through data-informed decisions by demonstrating initiative, accountability, collaboration, technical leadership, and strong stakeholder management. Accountabilities: Collaborate extensively with biostatisticians, phenomics scientists, and enviromics scientists to support advanced data analysis initiatives. Drive validation processes for digital phenomics traits across stage-gate milestones. Spearhead integration of genomic, phenomic, and environmental datasets into breeding workflows to enhance selection precision and strategic decision-making. Support continuous improvement through applied statistical concepts in data collection, experimental design, and analysis workflows across breeding teams. Work directly with plant breeders to evaluate new breeding strategies via stochastic simulations. Support the creation and implementation of predictive models and decision-support platforms customized for breeding program requirements. Oversee adoption of innovative technologies and methodologies for breeding decisions in direct partnership with breeding teams. Collaborate with data scientists, software engineers, and other domain experts to ensure breeding solutions are scientifically robust, accessible, and value-generating. Communicate findings and recommendations clearly to both technical and non-technical audiences. Promotes a safe work environment to maintain our strong safety culture. Qualifications PLEASE NOTE: Candidates must be legally authorized to work in the United States on a permanent basis without requiring current or future sponsorship (which also includes OPT/CPT and H-1B visas). Ph.D. in data science, plant breeding, quantitative genetics, or equivalent plus 2+ years of applied experience and deep technical understanding of commercial plant breeding and germplasm development processes. 3-5+ years of applied data science experience in plant breeding, agriculture, life sciences, or related domains with demonstrated impact on product development or operational optimization. Exceptional analytical capabilities with demonstrated success in independently delivering data-driven solutions through effective problem framing, data exploration, model development and validation, and generation of actionable insights. Experience working with large-scale phenomic, environmental, and genomic datasets. Working knowledge of statistical modeling, machine learning, or simulation approaches applied to biological systems. Demonstrated expertise using R or Python and SQL to build, manipulate, and analyze extensive datasets and/or develop data models within cloud computing platforms (AWS, Azure, GCP, Snowflake). Strong collaborative and communication capabilities, with evidence of successful cross-functional teamwork. Ability to manage multiple projects and deliver results in a dynamic environment. Experience with version control, CI/CD pipelines, and MLOps practices including GitLab/GitHub, model deployment, monitoring, and governance in production environments. Additional Information What We Offer: A culture that celebrates belonging and collaboration, promotes professional development and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs. Full Benefit Package (Medical, Dental & Vision) that starts your first day. 401k plan with company match, Profit Sharing & Retirement Savings Contribution. Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits. Syngenta has been ranked as a top employer by Science Journal. Learn more about our team and our mission here: https://www.youtube.com/watch?v=OVCN_51GbNI Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. WL: 4A #LI-Hybrid

Responsibilities

This role involves extensive collaboration with biostatisticians and phenomics/enviromics scientists to enhance breeding strategies through improved experimental design, analysis, and decision-making capabilities. Key tasks include driving validation processes for digital phenomics traits and spearheading the integration of genomic, phenomic, and environmental datasets into breeding workflows.