AI Intern - Energy Efficiency (M/F/N)

About the job

Company Description

The Syngenta Group, a global leader in agricultural technology and innovation, employs 60,000 people in more than 100 countries to transform agriculture through tailored solutions for farmers, society, and our planet. Our diversified portfolio includes seeds, crop protection products, nutritional products, agronomic solutions, and digital services, all designed to help farmers produce healthy food, feed, fiber, and fuel while preserving natural resources and protecting the environment. Our mission is to address critical challenges such as climate change and food security through sustainable practices and cutting-edge solutions, while preserving the planet's resources.

Syngenta Crop Protection, the largest unit of the Syngenta Group, is a leader in sustainable agricultural solutions, offering innovative products to keep crops healthy while minimizing environmental impact from planting to harvest.

Syngenta Monthey is a production site for active substances, with 900 employees. Its activities are mainly related to plant protection and, to a lesser extent, the Professional Products sector. Discover the activities, history, and challenges of the Monthey site via the YouTube video https://www.youtube.com/watch?v=qkZhG3x3Nz8

Syngenta has been ranked as one of the best employers by Science magazine and has received the "Friendly Work Space" label for all its Swiss sites.

Job Description

Job Purpose / Reason for the Position:

Develop and deploy advanced data analysis methodologies (Machine Learning, statistics) to optimize the energy performance of refrigeration installations and steam networks, use real-time data streams to detect early drifts, degradations (fouling), and anomalies (leaks), and actively contribute to the strategy of reducing energy consumption and the carbon footprint of the Monthey site by improving the efficiency of critical utilities.

Responsibilities

• Conduct a comprehensive diagnosis of the existing data infrastructure and KPI quality to identify optimization opportunities.
• Design and plan adapted analysis methodologies (predictive modeling, anomaly detection) for refrigeration and steam systems.
• Identify concrete action paths by exploring production data (temperatures, pressures, COP) in close collaboration with business experts and maintenance.
• Develop and deploy a Proof of Concept (POC) on a pilot installation in collaboration with development teams.
• Document the methodology and results obtained to establish an industrial deployment plan at the site level.
• Collaborate cross-functionally with Data Science, Engineering, and Maintenance teams to validate field hypotheses.

Qualifications

• Advanced mastery of Python and common Data Science libraries (Pandas, Scikit-learn, Numpy, Matplotlib, TensorFlow, PyTorch, etc.).
• Strong knowledge of statistical modeling and machine learning algorithms.
• Familiarity with database management and integration.
• Analytical skills, autonomy, and ease working in a multidisciplinary team.
• A strong interest in the industrial environment and energy efficiency issues.

• Degree: Final year Master’s student (or equivalent) in Data Science, Machine Learning, or related field.
• Languages: Fluent French. English (B2 level).
• Tools: Proficiency in standard IT tools and Data development environments.

Additional Information

• We offer a position that contributes to valuable and impactful work in a stimulating and international environment.
• A great working environment with an open culture and a diverse workforce where new ideas are always welcome.
• The opportunity to work with and learn from highly qualified and experienced employees and to acquire scientific and technical excellence.

Please send your complete application file with CV, cover letter, diplomas, and work certificates (if applicable).

Syngenta is committed to Diversity. We encourage all applications regardless of social and cultural background, age, gender, disability, sexual orientation, or religious beliefs.

#LI-Onsite