Data Scientist F/M

The CompanyAgriodor is an innovative, impact-driven, and multi-award-winning SME founded in 2019, based on research into the relationship between crop pest insects and plants.After an initial phase of laboratory and field development, the company patented its first solutions based on Kairomones and Allomones, which significantly reduce the use of phytosanitary products in crops — a major benefit for biodiversity. Agriodor is the first company worldwide to design this type of solution.This rapid progress enabled the company to raise funds and earn the French Tech 2030 label.With two distribution contracts already signed, Agriodor is now experiencing strong project acceleration. At the heart of this growth is the expansion of target insects and crops, including beet, potato, fruit trees (stone fruits, apple trees), berries, tropical fruits, and soon maize, soybean, and cotton.Agriodor target strong performance improvement of its activities with a more relevant use of data : data management, data science, AIPost DescriptionContext & Position OverviewThe Data scientist will lead the development and management of Agriodor’s proprietary and external datasets with the support and to the service of each domain : R&D teams (chemistry, entomology, agronomy, formulation) and other teams (marketing…)The objective of the Data Scientist, through its missions, are to :Ensure data-driven decision-making across the companyDevelop efficiency and robustness in data analysis within the companyPrepare Agriodor’s data architecture for future AI/ML applicationsSupport the deployment of AI tools and agents which are relevant to each domainTo Reach Those Objective, The Data ScientistWill report directly to the Chief Technology Officer (CTO)Will receive the support of the external CIOWill work closely with each domainWill structure the Data Department which will probably include recruitment needs to ensure that the above mentioned objectives are reachedKey Responsibilities Database Management & EvolutionDevelop, maintain, and optimize Agriodor’s database. This responsibility includes :Deploy Data architecture management good practices with appropriate processes to ensure data quality, integrity, security, completeness and compliance.Set protocols to cleanse historical data Ensure proper collection of all Agriodor’s relevant data with the help of each team and the relevant tools for data collection and data cleansingTrain teams to fully utilize tools deployed and empower them so that they can guarantee quality of uploaded dataIntegrate external datasets (agronomic, environmental, entomological, literature, climate data):Design scalable data structures to future AI deployment (machine learning models…)Data science & statisticsGive access to each team relevant data extracts and train them so that they are as autonomous as possible to perform the analysis they need for their workDevelop new data analysis methods (data science or biostat)Collaborate with teams to ensure statistical robustness in experimental design.Support internal reporting, publications, and regulatory dossiersTrain and support internal teams to strengthen statistical literacy across the company.AIConceive PoCs, Minimal Viable ProductsSupport CIO / in deploying relevant LLM and AI tools access to R&D Teams and other functions (Marketing, Back …)Support teams in the creation of their own agents…Contribute to long-term digital strategy, including predictive modeling with AI/ML Profile RequiredProfile & Requirements5 years+ of experience in data management, data sciences and statistical analysis.Entry level experience in AI/AgentsExperience in life sciences appreciatedMaster’s or PhD in Data Science, Applied Mathematics, Computational Biology, Bioinformatics, Biostatistics, or related field.Expertise in experimental design, advanced statistical modeling, and analysis.Experience with databases (SQL / No SQL), R, Python, SAS, visualization tools.Familiarity with ML frameworks (scikit-learn, TensorFlow, PyTorch) is a plus.Fluent in French and English.Strong analytical mindset, rigor, autonomy, teamwork, and communication skills.