Filtros

Ubicación

Industria

Comercio de granos y agroindustria (1362)Maquinaria, riego y equipo (1286)Alimentos y bebidas: proceso y empaque (1255)Insumos: semilla, nutrición y protección de cultivos (1174)Producción agrícola y hortofrutícola (142)Producción pecuaria, acuícola y sanidad animal (17)

Tipo de contratación

Tiempo completo (1993)Por temporada (36)Medio tiempo (26)

Modalidad

Presencial (7)Remoto (5)Híbrido (1)

Solo con sueldo visible

5236 vacantes

Ordenar:Destacadas Recientes Mejor sueldo

Volver a la lista

Syngenta

Computational Agronomy Scientist

Ubicación del puesto

Durham, NC. United States of America · United States

Publicada el hace 2 semanas

Investigación, desarrollo y mejoramiento genético

Descripción

Company Description

About Syngenta

At Syngenta Group, we're a global community of 56,000 innovators across 90 countries, united by a 250-year legacy of agricultural excellence. As the world's most local agricultural technology partner, we create tailor-made solutions that transform farming while protecting our planet, driven by our commitment to innovation, ethics, and integrity. Through our welcoming environment and varied perspectives, we pioneer breakthrough solutions for farmers, society, and future generations. Join our worldwide teams of agricultural pioneers in creating a more resilient and equitable food system for all.

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 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.

Qualifications

Required 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.

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: 5B

#LI-DNI

Vacantes del campo

Computational Agronomy Scientist

Descripción

Company Description

Job Description

Qualifications

Additional Information

Computational Agronomy Scientist

Descripción

Company Description

Job Description

Qualifications

Additional Information