
Statistician – Product Biology
- São Paulo - SP
- Permanente
- Período integral
- Independently guarantee good statistical practice is applied in Crop Protection Development activities, with special attention to design of experiments.
- Closely work with product-line teams in statistical and data-analytics work applied to development projects, beginning to end.
- In coordination with other functions, help in the transition to digital methods applied to agriculture (e.g., digital data capture, predictive modelling).
- Develop and implement a strategy to foster best practice in data analytics and participate in projects within the area of responsibility.
- Provide documentation, training material and deliver training courses to maintain and improve trial design and analysis standards for global/regional/country users (field scientists, technical managers, others as necessary).
- Provide the appropriate statistical input to various CP R&D projects (e.g., plot-size optimization, crop density & crop-protection program optimization, trial-placement optimization).
- Profound knowledge of statistics methods and their application in agronomy and/or biology.
- Proficiency in English is must.
- Advanced technical knowledge of the R statistical programming language. Other statistical suites (e.g., SAS, STATA, JMP) and programming languages (e.g., Python, Java, C++) are an asset.
- Good theoretical understanding of the principles of experimental designs applied to agronomic trials.
- Agile mindset, with focus on delivery of results in constant contact with stakeholders. Training in Agile Project Management methods and/or experience in start-up companies is an asset.
- Good knowledge of Git or other version-control systems for software development and reproducible-science work.
- Postgraduate degree in statistics or in natural sciences with a heavy component of statistics.
- Strong background in statistics- and data science-oriented programming languages (i.e., R, Python)
- Strong knowledge and understanding of statistical methods such as meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, design of experiments, etc.
- Strong knowledge of scientific modelling methods and their applications to applied questions.
- PhD is preferred.