Groupe Roullier

About the Company

An industrial, family-owned and independent Group, the Groupe Roullier achieves, thanks to its 10,000 employees, 70% of whom work internationally, 4.1 billion euros in consolidated turnover. Its diversified activities (soil, plant and animal nutrition, agri-food, renewable energies), centered on human needs, show the openness of a Group connected to the challenges of the future. With 109 production units all over the world, it is a Group, always on the move, which makes innovation and continuous improvement a daily challenge and which gives its leading activities (TIMAC AGRO, Phosphea, Magnesia, Agribusiness), as well as at its diversification center (Algology, Renewable Energies and Plastics), full autonomy and the freedom to test and move forward. Do better, do differently, exploring possibilities, this is the Groupe Roullier's way of acting.

Listed Jobs

Company Name

Groupe Roullier

Job Title

Stage - Agro-Data Scientist F/H

Job Description

**Job title:** Agro‑Data Scientist Intern (Female/Male) **Role Summary:** Support continuous improvement of an existing OAD tool that integrates multi‑temporal satellite vegetation indices, statistical methods, and machine learning. Conduct structured scientific analyses, evaluate potential enhancements, and propose actionable recommendations to agronomic experts. **Expactations:** (Expectations) * Complete a 4–6 month internship. * Collaborate closely with a senior data scientist and cross‑disciplinary agronomy team. * Demonstrate strong analytical rigor, curiosity, and autonomous problem‑solving. **Key Responsibilities:** * Analyze and refine statistical methodologies applied to agronomic and satellite data (confidence intervals, distribution fitting, robustness, bias, parameter sensitivity). * Design, implement, and evaluate machine‑learning models (classification, drift detection, time‑series analysis, multivariate approaches). * Assess data quality and reliability (outlier detection, quality filtering, impact of imperfect data on models). * Interpret vegetation indices and satellite imagery within agronomic context, understanding physical limitations and index complementarity. * Document findings, hypotheses, experimental design, and conclusions for operational use. **Required Skills:** * Proficient in Python; experience with Scikit‑learn, Pandas, NumPy, SciPy. * Strong applied statistics knowledge (hypothesis testing, confidence intervals, distribution theory). * Familiarity with machine‑learning pipelines and time‑series methods. * Ability to undertake a structured scientific workflow (hypothesis → experiment → critical analysis). * Excellent critical thinking, communication, teamwork, and proactive initiative. **Required Education & Certifications:** * Current enrollment in a Bac+4/5 (or equivalent) program in Data Science, Artificial Intelligence, Statistics, or an agronomy program with data‑science specialization. ---

St-malo, France

Hybrid

28-01-2026