Job DescriptionAt 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 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 domain's 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 workstream's 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 model's 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.
QualificationsRequired Qualifications:- Master's degree in Agronomy, Crop Science, Plant Pathology, Soil Science, or a related agricultural discipline; PhD preferred.
- At least five years of relevant professional experience-or equivalent demonstrated expertise-in 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.
WL: 5B#LI-DNI
