Computational Agronomy Scientist

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 Syngentas IT & Digital Team is seeking a Computational Agronomy Scientist in Durham NC. This role will lead complex and ambiguous agronomic initiatives from initial problem definition through implementation adoption and value realization.

In this senior individual-contributor role you will:

• Solve complex problems across crop growth physiology disease pest epidemiology nutrition abiotic stress and seed placement.
• Partner with stakeholders to define the right problem objective scope and success measures before work begins.
• Determine the scientific approach when an established method or solution does not exist.
• Lead multidisciplinary workstreams involving contributors across teams disciplines and geographic locations.
• Ensure the scientific integrity reproducibility implementation and adoption of agronomic models and recommendations.
• Develop scientific and technical standards rather than simply applying existing practices.
• Represent Computational Agronomy in cross-functional scientific and external forums.

This is a Work Level 5B individual-contributor role. It may include day-to-day direction of interns and contractors but does not include direct line management of employees.

Accountabilities:

Scope and Accountability

• Own the scientific integrity delivery adoption and value realization of assigned workstreams.
• Establish the scientific approach when no existing method adequately addresses the problem.
• Clearly document assumptions uncertainty limitations and conditions under which a model or recommendation is valid.
• Develop and advance the domains scientific analytical modeling and reproducibility standards.
• Build relationships with regional product platform commercial and scientific stakeholders.
• Provide onboarding technical guidance knowledge transfer and evidence-based feedback for workstream contributors.
• Create documentation processes and capabilities that remain valuable beyond the individual project or scientist.

Problem Framing and Scientific Direction

• Partner with stakeholders to define the underlying agronomic problem before developing a solution.
• Challenge requests constructively when the proposed objective or method does not address the actual need.
• Establish the workstreams objective scope success criteria deliverables and scientific boundaries.
• Determine the appropriate scientific approach and explain the alternatives considered.
• Define the model strategy including calibration protocols validation methods performance criteria and monitoring expectations.
• Identify data requirements gaps quality concerns fitness limitations and sources of uncertainty.
• Clearly communicate assumptions risks limitations and the models approved domain of validity.

Workstream Ownership and Delivery

• Lead multidisciplinary workstreams spanning multiple projects teams geographic locations and planning cycles.
• Manage the workstream from initial definition through development implementation adoption and value realization.
• Identify negotiate and sequence dependencies involving teams that do not report directly to the role.
• Prioritize work based on scientific value business impact customer needs resource constraints and technical dependencies.
• Make trade-offs transparent and ensure contributors remain focused on agreed outcomes.
• Deliver workstreams according to established specifications quality standards and deadlines.
• Confirm that solutions are adopted produce measurable value and leave behind sustainable documentation and capability.

Scientific and Methodological Leadership

• Guide advanced experimental analytical statistical and modeling approaches across studies and workstreams.
• Design or oversee multi-location field studies and evaluate the quality of their resulting data.
• Assess emerging scientific and computational methods based on evidence and practical agronomic value.
• Review models analytical methods documentation and code developed by other contributors.
• Strengthen scientific analytical modeling code-quality and reproducibility standards across the team.
• Ensure workstream results are scientifically defensible reproducible and appropriately documented.
• Capture and share negative or inconclusive findings so the organization can learn from them.

Stakeholder Partnership and Representation

• Manage relationships with stakeholders across regional product platform commercial and scientific functions.
• Navigate conflicting priorities and recommend an appropriate path based on evidence and business value.
• Set realistic expectations and decline requests when scientific evidence does not support the proposed direction.
• Build alignment and influence technical scientific and business decisions without relying on formal authority.
• Translate complex science uncertainty and model limitations into decision-ready recommendations.
• Present workstream strategy progress outcomes and risks to senior audiences.
• Represent Computational Agronomy in cross-functional initiatives external partnerships and scientific forums.

Coordination Mentoring and Capability Building

• Coordinate contributors across disciplines teams and locations while maintaining clear priorities and accountability.
• Define sequence review and accept work completed by interns contractors and other workstream contributors.
• Provide effective onboarding technical direction coaching and ongoing knowledge transfer.
• Give timely specific and evidence-based performance feedback to the appropriate hiring or people manager.
• Mentor scientists and technical contributors through scientific guidance model review code review and constructive feedback.
• Build team capability by sharing reusable methods standards documentation and lessons learned.
• Support a collaborative environment in which contributors can challenge assumptions and continuously improve their work.

Innovation and AI Adoption

• Identify emerging scientific statistical computational and agronomic methods relevant to the organization.
• Evaluate new methods based on scientific evidence scalability business value and practical applicability.
• Convert promising research and technical approaches into repeatable working practices.
• Use generative AI and AI-assisted coding to accelerate research analysis documentation and development.
• Demonstrate effective AI applications and help other contributors build confidence and fluency.
• Establish appropriate quality controls for AI-assisted scientific and technical work.
• Contribute expertise to departmental initiatives beyond the immediate workstream.

Qualifications :

Required Qualifications:

• Masters degree in Agronomy Crop Science Plant Pathology Soil Science or a related agricultural discipline; PhD preferred.
• At least five years of relevant professional experienceor equivalent demonstrated expertisein agricultural research digital agronomy modeling validation or agronomic decision-making.
• Advanced expertise in at least one agronomic domain with experience applying that knowledge to complex and ambiguous problems.
• Demonstrated success leading technical projects or cross-functional workstreams from concept through implementation adoption and value realization.
• Strong proficiency in Python and/or R applied statistics and AI or machine-learning methods used with agricultural data.
• Experience designing complex experimental and statistical approaches including multi-location field studies and reproducible analytical workflows.
• Demonstrated ability to influence without authority mentor technical colleagues and communicate complex scientific findings in written and verbal English.

Agronomic Knowledge

• Advanced knowledge of crop-production systems such as corn soybeans wheat cotton or canola.
• Strong understanding of crop growth stages agronomic management practices yield-limiting factors and field-level decision-making.
• Deep knowledge of insect disease and weed management including lifecycles economic thresholds return on investment and integrated pest-management principles.
• Understanding of crop-protection practices involving fungicides herbicides insecticides biologicals and seed treatments.
• Experience using disease or pest models to forecast outbreaks and guide agronomic decisions.
• Working knowledge of agrometeorology and its application to crop management and pest forecasting.
• Familiarity with field research active scouting IoT devices sensors and soil and plant-sampling technologies.

Desired Qualifications:

• Experience with crop-simulation frameworks such as DSSAT or APSIM.
• Knowledge of Bayesian hierarchical or other advanced model-calibration approaches.
• Experience in epidemiology pest ecology geospatial datasets or geospatial analysis.
• Experience with cloud environments such as Amazon SageMaker.
• Experience partnering with data and machine-learning engineers to deploy and monitor model solutions.
• Experience coordinating contractors interns external researchers or academic partners.
• Record of scientific publication conference presentation or work within Agile software-development environments.

Additional Information :

• Authorized to work in the United States without sponsorship

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:  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: 5B

#LI-DNI

Remote Work :

No

Employment Type :

Full-time