Turn complex R&D datasets into robust predictive models and scientific insights as a Computational Life Scientist at Royal Canin in Aimargues, near Montpellier, France. This full-time, permanent role combines life sciences expertise with advanced statistics, machine learning, and AI to accelerate innovation in pet health and nutrition.
Requirements
Education: Master's degree or PhD (preferred) in biology, veterinary medicine, animal health, physiology, biochemistry, microbiology, nutritional sciences or a related discipline, with formal training in quantitative fields.
Experience: Minimum of 3+ years applying machine learning, advanced analytics, or computational modeling in a life science, healthcare, biotech, pharmaceutical, nutrition, food science, veterinary, or related R&D environment.
Technical Skills: Strong understanding of machine learning algorithms, statistical inference, model validation, and cloud-based analytical environments.
Preferred Skills: Experience with explainable AI, multi-omics integration, natural language processing, or deep learning.
Skills & Responsibilities
Model development: Lead the design and evolution of statistical and machine-learning models on complex R&D datasets to drive scientific discovery.
Feature engineering: Co-design scientifically meaningful variables with domain experts to improve model performance and interpretability.
Model validation: Establish robust validation frameworks including cross-validation and reproducibility assessments to ensure scientific rigor.
Data integration: Integrate data across clinical studies, animal facilities, formulation systems, and omics databases.
Potential transition routes: People from Bioinformaticians, Data Scientists in Healthcare, or Quantitative Biologists may bring strong computational and domain skills, alongside meeting the mandatory life sciences and machine learning requirements.
Job description
Job Description:
Segment : Royal Canin
Contract : CDI/Permanent
Location : Aimargues, near to Montpellier
Royal Canin R&D generates large, complex datasets across nutrition, ingredients, product design, manufacturing, clinical and biological studies, microbiome, metabolomics and pet owner observations. The Computational Life Scientist turns these data into robust predictive models and scientific insights, combining life sciences expertise with advanced statistics, machine learning and AI. Working in tandem with the Augmented Algorithms (AA) Food Scientist, the role leads modeling choices, methodological validation, interpretability and reproducibility, to accelerate innovation and evidence generation across the R&D portfolio.
What are we looking for?
Essential dual profile, life sciences first: A Master's degree or PhD (preferred) in biology, veterinary medicine, animal health, physiology, biochemistry, microbiology, nutritional sciences or a related life sciences discipline, combined with formal training or demonstrated professional expertise in statistical modeling, machine learning/AI, bioinformatics, biostatistics or another quantitative field.
Minimum Experience: 3+ years applying machine learning, advanced analytics or computational modeling in a life science, healthcare, biotech, pharmaceutical, nutrition, food science, veterinary or related R&D environment, including end-to-end predictive modeling projects from problem framing to interpretation and deployment.
Demonstrated ability to translate biological, veterinary, health or nutritional questions into testable quantitative problems and interpretable models, in close collaboration with domain scientists.
Experience developing predictive models on structured and unstructured scientific data (e.g., biological, clinical, nutritional, omics or industrial research datasets).
Strong understanding of machine learning algorithms, statistical inference, model validation and performance evaluation.
Familiarity with cloud-based analytical environments and scalable computing platforms.
Experience with explainable AI and model interpretability approaches is preferred.
Experience with multi-omics integration, natural language processing or deep learning is preferred.
What will be your key responsibilities?
Modeling & Machine Learning
· Model development: Lead the design, development and evolution of statistical and machine-learning models on R&D datasets, building where relevant on exploratory models, scientific rules or prototypes developed with the AA Food Scientist and other scientific experts.
· Methodology selection: Select and apply the most appropriate approaches, including supervised and unsupervised machine learning, ensemble methods, deep learning, survival analysis, Bayesian methods and multivariate statistics.
· Feature engineering: Co-design scientifically meaningful variables with domain experts, then lead their engineering, selection and testing to improve model performance and interpretability.
· Model validation: Establish robust validation frameworks (cross-validation, external validation, sensitivity analyses, reproducibility assessments) to ensure scientific rigor and business confidence.
Scientific Discovery & Data Integration
· Hypothesis-driven discovery: Apply predictive analytics and AI to identify patterns and predictive signals in R&D data, and test hypotheses with domain experts to assess their biological, nutritional and product relevance.
· Multi-source data integration: Integrate data across clinical studies, animal facilities, formulation systems, ingredient databases, microbiome, metabolomics and other internal or external sources.
· Evidence translation: Turn complex analytical outputs into interpretable evidence, scientific conclusions, algorithm requirements and actionable recommendations.
· Publication support: Contribute analytical expertise to peer-reviewed publications, conference presentations, intellectual property and scientific substantiation dossiers.
R&D Partnership & Project Delivery
· Partnership with the AA Food Scientist: Work as a scientific and modeling pair across the full algorithm lifecycle, from problem framing and prototyping to validation, interpretation and improvement, with accountabilities adapted to each use case.
· Problem framing: With the AA Food Scientist, Research Scientists and other experts, translate scientific and business needs into well-defined modeling problems: target, relevant predictors, intended use, validation criteria, decision thresholds and known limitations.
· Study design input: Provide modeling recommendations during study design to maximize the future predictive value and analytical robustness of the data.
· Analytical consulting: Advise R&D teams (Research, Product Design, Facilities, Clinical Studies, Deployment) on model assumptions, data requirements, interpretation of results and limitations.
Computational Innovation & Continuous Improvement
· Technology watch & benchmarking: Monitor and evaluate emerging AI methods (including generative and agentic AI), computational biology approaches and external tools, assessing their relevance for nutrition, animal health and algorithm development.
· Modeling standards: Contribute to best practices for coding, model documentation, reproducibility and analytical governance across the team.
· Automation: Develop scalable analytical workflows and reusable modeling pipelines to improve efficiency and accelerate project delivery.
What can you expect from Mars?
You will work in a multicultural environment (more than 40 nationalities) and where your dog will be welcomed by more than 100 other Associates’ dogs at the office!
You will work in a unique campus, which gathers more than 800 Associates working for: the international HQ, the French subsidiary, two laboratories, a kennel and a cattery with more than 200 cats & dogs, and finally the most important factory of the Royal Canin group
A unique and ideal work environment to become the leader in health through nutrition for cats and dogs!
You will evolve in the heart of an innovative production plant that will challenge you, on a daily basis
Royal Canin's CSR commitments, to build a better world for tomorrow:
Since 2015, no waste to landfill is made by the 16 Royal Canin factories in the world
All Royal Canin products manufactured in Europe are ISO 14001 certified from design to factory gate
Since 2017, Royal Canin has supported the K-DOG program of the Curie Institute, which trains dogs to detect breast cancer via olfaction.
