Senior Data Analyst – Controlled Environments

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

The Data Analyst enables reliable, data-driven decisions across the Trait Introgression (TI) and Double Haploid (DH) pipelines at the Trait Conversion Accelerator (TCA) in Nampa, Idaho. The role core responsibility is to transform operational, phenotypic, genotypic, and inventory data into trusted insights that strengthen operational excellence, productivity, traceability, and delivery.

The Data Analyst defines and governs data architecture; integrates diverse data sources into scalable analytical workflows; and develops reporting tools that support planning, project prioritization, workload and budget management, resource allocation, and continuous improvement. Combining programming and analytical expertise with a strong understanding of crop biological systems, this role converts complex data into clear performance indicators and actionable recommendations for Field Crops controlled-environment operations.

Accountabilities:

• Data architecture and operational data products: Design, develop, and maintain trusted datasets, scalable dashboards, and recurring reports supporting DH and TI workflows-from project initiation through controlled-environment execution, genotyping, phenotyping, selection, advancement, and hand-off.
• Performance management: Define, monitor, and report operational and business KPIs across DH and TI execution pipelines. Compare performance against plans, targets, and internal thresholds to identify improvement opportunities, strengthen execution, and maximize product success.
• Workload planning and capacity monitoring: Analyse greenhouse footprint, capacity, occupancy, labor demand vs availability, planting schedules, cycle duration, environmental conditions, and seasonal constraints. Track actual versus planned performance to identify risks within controlled-environment operations and external dependencies, such as genotyping. Integrate these data sources into decision-support tools that enable scenario modelling and “what-if” simulations to optimize operational execution and increase productivity.
• Data quality and traceability: Establish robust data-quality standards, cross-check mechanisms, and automated controls to identify, prevent, and correct data collection, recording, transformation, or analytical errors. Ensure data integrity, consistency, and end-to-end traceability across digital workflows.
• Controlled-environment and laboratory analytics: Analyse controlled-environment and laboratory data to identify performance trends, support structured root-cause analysis, and establish data-driven productivity targets based on available operational data. Translate findings into actionable improvement priorities and measurable performance expectations.
• Data-driven decision support: Translate complex controlled-environment and operational data into concise, action-oriented insights and recommendations. Apply AI tools and applications to identify patterns, generate predictive insights, evaluate scenarios, and strengthen data-driven decision-making. Support optimization of footprint utilisation, resource allocation, project execution, risk mitigation, and digital safeguards that reduce product vulnerabilities.

PLEASE NOTE: Candidates must reside in and be permanently authorized to work in the United States without current or future employer sponsorship. This includes, but is not limited to, OPT, CPT, and H-1B visa holders.

• M.S. or Ph.D. in Data Science, Data Engineering, Statistics, Data Science, Industrial Engineering, or related field.
• Minimum 2 years of experience in data analytics, statistics, computational biology, or related field preferred.
• Experience modelling biological systems and developing simulation and predictive algorithms, including AI-based, highly desired.
• Experience in data manipulation, integration, analysis, and interpretation; graphic summary and reporting; dashboard development and management for visualization and communication.
• Ability to translate complex analytical outputs into concise, action-driven recommendations.
• Experience with Microsoft-based data tools or related analytics platforms.
• Familiarity with data-collection devices and process improvement.

What We Offer:

• A culture that celebrates belonging & 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.

Use of AI in Our Recruitment Process

At Syngenta, we use artificial intelligence (AI) to screen applications for high-volume roles.

When you submit an application, AI technology assesses your profile against the job requirements and generates a matching score. This score reflects how closely your qualifications, skills, and experience align with the role's criteria as defined in the job description.

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