We have an exciting opportunity for data scientists specialized in machine learning with interests in agriculture and environment to join our Global Data Analytics and Predictive Science Team! Within this role you will be working within Global Product Biology and across R&D functions to bring new insights for active ingredient development by developing predictive models while guiding design optimization and development of novel crop protection this role you will not only apply existing ML approaches but also develop innovative solutions to unique agricultural challenges that havent been solved before. Key responsibilities will include:
- Designing and implementing innovative machine learning solutions to solve complex agricultural and environmental challenges that often require novel approaches.
- Developing and optimizing predictive models using structured historical cross-functional data from field trials laboratory experiments and environmental measurements.
- Translating agricultural/environmental problems into data science solutions in collaboration with domain experts.
- Supporting experimental design to ensure data suitability for modeling purposes.
- Working with IT teams on end-to-end ML projects from data preparation to model deployment and monitoring.
- Presenting insights and recommendations to stakeholders across different functions.
- Engaging 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
- PhD in Computer Science Data Science Machine Learning or related field (or . with extensive hands-on ML experience).
- Proven experience in developing machine learning models particularly:
- Supervised learning (Random Forests XGBoost SVM etc.).
- Clustering and dimensionality reduction.
- Time series analysis.
- Handling hierarchical/nested data structures.
- Strong programming skills in Python and experience with ML frameworks (scikit-learn pandas etc.).
- Demonstrated ability to develop novel solutions beyond applying existing frameworks.
- Creative problem-solving mindset and ability to think outside standard approaches.
- Experience in adapting and modifying algorithms to suit specific problem requirements.
- Passion for emerging technologies including Generative AI and other innovative approaches.
- Strong communication skills to explain technical concepts to non-technical stakeholders.
- Ability to visualize and story-tell with data to communicate results to parties with varying levels of technical proficiency.
- Interest in agriculture environmental science or related fields.
Desired Qualifications
- Experience with version control (Git) and ML experiment tracking.
- Experience working in cross-functional teams.
- Familiarity with experimental design or statistical analysis.
- Experience with cloud computing platforms (AWS Azure etc.) and querying (e.g. SQL).
Additional Information :
Additional locations in Europe (Germany France Spain Portugal Italy Poland) can be also considered.
Application & Recruitment Process
Due to exceptionally high interest in this position we will only consider applications that include a covering letter explaining your motivation for the role and outlining your suitability. Upload your CV and write your cover letter in the Message to the Hiring Team section on the application page.
What we offer
- Extensive benefits package.
- Flexible working.
- We offer a position which contributes to valuable and impactful work in a stimulating and international environment.
- Learning culture and wide range of training options.
Syngenta has been ranked as a top 5 employer and number 1 in agriculture by Science Journal.
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. Learn more about our D&I initiatives here: Work :
Yes
Employment Type :
Full-time