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.
