Consultant

The objective of this consultancy is to provide technical support to CIMMYT-DCP in the analysis of breeding trial data, ensuring that datasets are quality checked, analyzed using appropriate statistical methods, and translated into clear outputs for breeder decision-making and program reporting.

Scope of work

The consultant will work closely with the CIMMYT quantitative genetics, biometrics, data management, and breeding teams to support the following areas:

1. Review and organize breeding trial datasets received from different crops, locations, partners, and seasons.

2. Conduct data quality checks, including review of trial design, trait structure, missing values, outliers, plot-level inconsistencies, genotype naming, location metadata, and treatment information.

3. Clean and prepare datasets for statistical analysis according to agreed CIMMYT workflows.

4. Conduct appropriate statistical analyses for breeding trials, including single-site analysis, across-location analysis, and estimation of adjusted means or BLUPs where applicable.

5. Generate analysis outputs required for product advancement, breeder review, reporting, and decision-making.

6. Document analysis procedures, scripts, assumptions, and outputs to ensure reproducibility and handover to CIMMYT teams.

7. Support harmonization of analysis outputs across crops and projects. 8. Participate in regular technical check-ins with CIMMYT-DCP staff to review progress, resolve data issues, and prioritize assigned datasets. Specific deliverables

The consultant will be expected to deliver the following:

1. Data inventory and review: A reviewed list of assigned datasets, including crop, location/instance, trial type, trait count, data status, and analysis requirements.

2. Data quality control output: QC reports for assigned datasets, documenting missing data, outliers, design issues, genotype inconsistencies, metadata gaps, and recommended corrections.

3. Cleaned analysis-ready datasets: Curated datasets prepared for statistical analysis, with documented changes and assumptions.

4. Trial-level statistical analysis: Analysis outputs for assigned single-location trials, including adjusted means or relevant model outputs, summary statistics, trait-level diagnostics, and interpretation notes.

5. Multi-location / multi-environment analysis, where applicable: Across-location analysis outputs for assigned datasets, including genotype performance summaries, variance components or model summaries, and outputs suitable for breeder review.

6. Breeder-ready result files: Clean Excel/CSV output files and summary tables that can be directly shared with breeding teams for advancement decisions.

7. Reproducible scripts and documentation: R, Python, or agreed workflow scripts with clear documentation, folder structure, input/output descriptions, and versioned analysis notes.

8. Monthly progress summary: Brief monthly progress report summarizing datasets analyzed, outputs delivered, pending issues, and recommendations for improving data analysis workflows.

9. Final consultancy report: A concise final report summarizing completed analyses, key outputs, unresolved data issues, recommendations, and suggested improvements for future CIMMYT-DCP data analysis processes.

Required qualifications and experience

The selected consultant should have:

1. PhD in quantitative genetics, biometrics, statistics or a related field.

2. At least 5-6 years of relevant experience in plant breeding data analysis, quantitative genetics, or biometrics.

3. Demonstrated experience analyzing multi-location crop breeding trials, selection indices & generating templates for advancement decisions

4. Strong working knowledge of experimental designs commonly used in plant breeding, including alpha lattice, randomized complete block design, augmented design, and multi environment trials.

5. Strong skills in R and/or Python for data cleaning, statistical analysis, automation, and reproducible reporting.

6. Experience with mixed models, BLUPs/BLUEs, genotype-by-environment analysis, trait summaries, and breeder-oriented reporting.

7. Experience working with CGIAR, NARES, seed systems, or international agricultural research programs is highly desirable.

8. Familiarity with breeding data systems, BioFlow, FAIR data principles, and structured data management practices is desirable.

9. Excellent written and verbal communication skills in English.

10. Ability to work independently, meet deadlines, and deliver high-quality outputs with limited supervision.

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