IRS26127 Maize Breeder

• A field-based breeder that drives step change breeding improvements using variety of genetic tools.
• Assist in implementing breeding pipelines that use machine learning, genomic prediction and GXEXM.
• Apply scientific rigor to selection accuracy, cycle time reduction, and gain per year.
• Ensure contemporary breeding and genetic methods are deployed in breeding pipelines.
• Contribute to global breeding system design, ensuring harmonization and cross-learning across geographies.
• Deliver Climate -resilient, resource-efficient germplasm.

• Design and implement MAS and genomic selection, haplotype tracking, DH, speed breeding, high throughput phenotyping and trait introgression methods in breeding pipelines.
• Collaborate with teams on gene discovery and functional validation projects.
• Ensure molecular tools are applied to farmer-relevant traits in alignment with program priorities.

• Implement breeder-focused dashboards that evolve into strategic decision tools
• Translate high-dimensional data into intuitive visuals for breeders, donors, and policymakers.
• Set global standards for data quality, traceability, and analytics
• Work closely with data science and informatics teams to deploy AI/ML models for trait prediction, parent selection, and resource optimization.
• Champion continuous improvement of maize breeding informatics platforms and data governance.

• Integrate physiological traits and agronomic management into ML-based predictions.
• Ensure models perform across all target environments including marginal environments where CIMMYT germplasm helps all farmers.
• Align breeding outputs with sustainable intensification goals.

• Mentor early-career scientists and students in maize breeding including molecular breeding, quantitative genetics, and data science applications.
• Build institutional capacity & partnerships with NARES partners, SMEs and universities in advanced maize breeding tools.
• Publish in top-tier journals on breeding and genetic gain, while developing breakthrough hybrids and lines.
• Guide large-scale proposals with compelling, evidence-based ROI justification
• Serve as Global Ambassador for CIMMYT Breeding.

• PhD in Plant Breeding, Genetics, Genomics, Data-Enabled Crop Improvement, or a closely related discipline.
• Minimum 2 years of post-Ph. D. experience in maize breeding or related crop improvement programs for tropical environments.
• Demonstrated success in deploying molecular and genomic tools within operational breeding pipelines.
• Track record of using data-driven and predictive approaches to improve breeding efficiency and genetic gain.
• Experience in collaborating with NARES, including public & private sector institutions
• Experience in collaborating across disciplines, including genomics, pathology, physiology, data science, and digital agriculture teams.
• Evidence of leadership in guiding technical innovation, mentoring scientists, and influencing program strategy.
• Advanced understanding of molecular breeding and quantitative genetics, including - Marker-assisted selection (MAS), Genomic selection (GS) and genomic prediction, QTL discovery, haplotype analysis etc.
• Strong applied knowledge of breeding data systems, including breeding informatics and database design, experimental design and multi-environment trial analysis and integration of phenotypic, genotypic, and environmental data
• Practical knowledge in data science methodologies, such as - statistical modelling and predictive analytics for genetic gain, application of machine learning algorithms for parent selection, line advancement, and performance prediction, working familiarity with AI-enabled breeding tools.
• Knowledge of data analysis software like R, SAS, and Genstat preferred.
• Good reporting and communication skills in English (working language).
• The selected candidate should exhibit the following competencies: Critical and Creative Thinking, Problem Solving and Decision Making, Teamwork, and Communication.

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