
Data Analytics and Predictive Science Lead
- São Paulo - SP
- Permanente
- Período integral
- Works in multidisciplinary project teams to bring data analytics and modelling skills to development and optimization of crop protection product candidates.
- In collaboration with the Digital Global Product Biology team and Regional counterparts, champion the Digital Transformation initiatives by driving collaboration efforts on connection points with different parts of R&D and Digital.
- In collaboration with the Product Biology community and the BITS platform product owners, identify opportunities for standardized improvements on protocol and experimental design, data quality and reporting. Co-develop and enable more effective trialing process.
- Interacts with product biology managers and other domain experts to understand scientific problems and identify opportunities where the adoption of models will drive value for the business.
- Works with colleagues in adjacent fields of data analytics and predictive science to investigate interdisciplinary and multiscale modelling approaches to solve fundamental challenges in active ingredient development for crop protection
- Provides expertise and guidance to scientists in the design of experiments and field trials for modelling purposes
- Drives strategic business initiatives across Crop Protection R&D e.g. working with physical chemistry, biokinetic and environmental data to model field trials.
- Merges data from multiple data sources together to prepare them for analysis
- Monitors new developments in the field and maintains awareness of modelling approaches taken by other companies, vendors, and academia. Explores new analytical tools, methodologies or approaches to understand their effectiveness and how to deploy them
- Strong academic foundation in statistics and predictive modelling to postgraduate level or natural science PhD;
- Knowledge of relevant data-science, analytics and modelling tooling (e.g. Python, R, Matlab, etc.);
- Scientific domain knowledge in relevant field such as agronomy, biology, chemistry, etc to be able to effectively work with crop protection scientists;
- Knowledge of model validation, assessing robustness and machine learning techniques applied to biological sciences;
- Experience in developing models relevant to biological or crop protection outcomes (e.g. epidemiology, pest or disease modelling);
- Experience in analysing and extracting insights and new understanding from data;
- Experience in working independently and to a high level of accuracy;
- Experience of collaborating with scientists and data scientists to solve complex problems;
- Experience of communicating scientific and data concepts to specialist and non-specialist audiences.