Modeling Scientist - Agriculture at Syngenta Group

Bracknell, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data-Driven Predictive Modelling, Python, Machine Learning, Deep Learning, Biological Performance, Ecological Modelling, Data Analysis, Visualization, Analytical Problem-Solving, Collaboration, Communication, Adaptability, Innovative Thinking, Statistical Analysis, Environmental Sciences, Crop Protection

Industry

Farming

Description

Company Description Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow productively and sustainably. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers. Our 17,900 employees serve to advance agriculture in more than 90 countries around the world. Syngenta Crop Protection is headquartered in Basel, Switzerland, and is part of the Syngenta Group. Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet. Join us and help shape the future of agriculture! Job Description Position: Modelling Scientist - Agriculture Location: We could consider candidates based at additional locations within Europe Application process: Carefully read instructions in "Additional Information" section We have an exciting opportunity for a Modelling Scientist to join our Global Data Analytics & Predictive Science Team! Within this role you will create, train and validate predictive models to uncover patterns and deliver new data-driven insights for active ingredient development across R&D functions. You will be asked to interpret the outcome of scientific experiments with the help of mechanistic understanding and machine learning / deep learning approaches. Your work will bring forward our understanding of biological performance in crop protection and guide design, optimization and development of novel crop protection solutions. Key responsibilities will include: Interacting with domain experts to understand scientific questions and identifying modelling opportunities to drive business value; Driving strategic business initiatives across Crop Protection R&D by interpreting physical chemistry, biokinetic and environmental data to support decision taking and design laboratory, glasshouse and field trials; Guiding technical managers in designing field trials aimed at validating scientific hypotheses and model predictions; Working with R&D IT and software developers to improve data-models integration and to deploy applications tailored on shareholders’ needs; Monitoring and exploring new modelling approaches, analytical tools and methodologies; Engaging with high-priority digital transformation projects to understand opportunities to accelerate the impact of data science for predictive field trialing; Working with colleagues and external collaborators understanding their complementary capabilities and integrating them into projects and initiatives. What we are looking for Strong foundations in data-driven predictive modelling at postgraduate level (Ph.D. in physics, mathematics or related theoretical sciences); Years of experience in the use of the main data-science, -analytics, modelling and visualization Python libraries (Pandas/Polars, SciPy, MatPlotLib); Scientific domain knowledge in related fields such as biology, (bio)chemistry, environmental sciences; Prior experience in developing models relevant to biological or crop protection outcomes (e.g. ecological modelling, ecotoxicology, environmental fate, epidemiology, disease modelling, PKPD) is a plus; Knowledge of data analysis and extracting data insights and new understanding, while communicating scientific and data concepts to specialist and non-specialist audiences; Adaptability to different business challenges and data types / sources and to learn and utilize a range of different analytical tools and methodologies; Ability to visualize and story-telling with data to communicate results to shareholders with different levels of technical proficiency; Analytical problem-solving skills with innovative thinking, while effectively collaborating across diverse teams and managing multiple priorities in a multicultural scientific environment. Additional Information Application process Due to exceptionally high interest in this position, we will only consider applications that include: (1) a CV, (2) a cover letter explaining your motivation and suitability for the role, and (3) a one-page document in which you tell us how (with which tools and algorithms, following which strategy) you would start exploring a 13Kb CSV dataset containing efficacy field trial results for a novel crop protection product including assessments for multiple crop types, trial sites and weather conditions. Please upload your CV, your cover letter and the one-page document in separate files named “CV_Your-Name", “Cover_Letter_###”, and “Answer_###”, replacing ‘###’ with your family name. Our site We could consider candidates based at additional locations within Europe. You may be required to travel to international R&D locations and to work with collaborators globally. What we offer Extensive benefits package including a generous pension scheme, bonus scheme, private medical & life insurance (depends on the contracting country). Flexible working. A position which contributes to valuable and impactful work in a stimulating and international environment. Learning culture and a wide range of training options. Syngenta has been ranked as a top 5 employer and number 1 in agriculture by Science Magazine for the 8th consecutive year.

Responsibilities

The Modelling Scientist will create, train, and validate predictive models to uncover patterns and deliver insights for active ingredient development. They will interact with domain experts and guide technical managers in designing field trials to validate scientific hypotheses.