Company Description

As a world market leader in crop protection, we help farmers to counter the threats to our crops and ensure enough safe, nutritious, affordable food for all – while minimizing the use of land and other agricultural inputs. Syngenta Crop Protection keeps plants safe from planting to harvesting. From the moment a seed is planted through to harvest, crops need to be protected from weeds, insects and diseases as well as droughts and floods, heat and cold. Syngenta Crop Protection is headquartered in Switzerland.

Job Description

MAKE A DIFFERENCE

As a Data Scientist, you will work across R&D functions to bring new insights for active ingredient development by developing predictive models allowing faster and higher quality decisions.

You will develop novel machine-learning approaches to build a data-driven understanding of biological performance in crop protection, and to guide design, optimization and development of novel crop protection solutions.

As part of the predictive science team you will be able to influence data strategy at Syngenta and provide expertise and guidance to scientists in the design of experiments and field trials for modelling purposes.

Responsibilities:

• Works in multidisciplinary project teams to bring data analytics and modelling skills to the discovery, optimization and development of crop protection leads or product candidates
• Interacts with 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 predictive science to investigate interdisciplinary and multiscale modelling approaches to solve fundamental challenges in active ingredient development for crop protection
• Drives strategic business initiatives across Crop Protection R&D e.g. working with physical chemistry, biokinetic and environmental data to model laboratory, glasshouse and field trials.
• Works with R&D IT and software developers to build data connections and deploy customer facing applications
• 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
• Works with external collaborators to understand and integrate their complementary capabilities into projects and programs
• Acquire knowledge from Syngenta internal expert modellers to build personal network and quickly get up to speed with existing modelling opportunities, and to acquire domain knowledge
• Support active ingredient projects by using analytical and modelling methods
• Engage with high-priority digital transformation projects to understand opportunities to accelerate the impact of data science for predictive field trialing.

Qualifications

WHAT WE ARE LOOKING FOR:

• Strong academic foundation in data science and predictive modelling to postgraduate level or natural science PhD (ecology, env. science, biology or chemistry)
• Knowledge of relevant data-science, analytics and modelling tooling (e.g. Python, R, Matlab, etc.)
• Scientific domain knowledge in relevant field such as biology, chemistry, environmental science etc to be able to effectively work with crop protection scientists 
• Knowledge of model validation and assessing robustness
• Experience in developing machine-learning models relevant to biological or crop protection outcomes
• Experience in analyzing and extracting insights and new understanding from data.
• Experience of communicating scientific and data concepts to specialist and non-specialist audiences
• Strong analytical thinking and problem solving skills, ability to think “out of the box” to develop new solutions
• Able to learn and utilize a range of different analytical tools and methodologies – not fixed in a particular methodology

Additional Information

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.