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Computational Horticultural Modeler
3 months ago
As a Computational Horticultural Modeler (Data Scientist) for our Computational Agronomy organization, you will help enable the integration of data sources with next-generation technologies across functional boundaries.
You will collaborate and partner with Syngenta Professional Solutions to deliver artificial intelligence (AI), machine learning (ML), and predictive analytics models across various Horticultural and Turf use cases.
For example, you may leverage data from IoT sensors for moisture probes, irrigation, disease, or insect management.
Through the direction provided by Syngenta Professional Solutions leaders, you will leverage critical thinking skills to innovate solutions for use cases that impact business outcomes.
Lead and contribute to a culture of creativity and expert support, enabling yourself and the team to identify, design, and deliver appropriate datasets and linkages for complex data.
Critical success factors & key challenges:
Clearly conveying the message to product and professional solutions lead about the scope and risk of the problem statement.
Completeness of the data to test the feasibility of development.
Implementation and validation of the crop science, machine learning, and rules-based models.
Collaboration with product and digital lead to build the needed solutions.
This is a remote work position (USA or Europe) and an individual contributor role. Must be authorized to work in your respective country. No visa sponsorship will be provided at this time. Around 10% travel may be required.
Duties:
Conceptualize, analyze, and develop new models and capabilities that will leapfrog traditional approach-based modeling and accelerate new ways of thinking.
Work with Syngenta Professional Solutions to understand their needs to develop new or improve existing solutions around data and analytics.
Develop solutions to meet business requirements, involving translation of requests into use cases, test cases, preparation of training data sets, and iterative algorithm development.
Remain current on new developments in data science, deep learning, and ML engineering.
