R&D Next-Gen PetCenter Method Engineer (Wet Food Transformation)

Job Description: Segment: Royal Canin Localisation: Aimargues (30 minutes from Montpellier) Type of contract: permanent contract To accelerate Royal Canin’s Wet Food Transformation, this role harmonizes existing and designs, deploys, and validates next‑generation Pet Center standard protocols and on‑site measurement/data‑capture capabilities across Royal Canin Pet Centers and the Pilot Plant, relying on the data of a broader Petcare facilities ecosystem to enable faster, more predictive product‑performance learning loops and improved speed to market.

It enables protocol harmonization—or proof of parity—where Royal Canin specifics are critical (e.g., responsible feeding for wet products), and acts as the on‑site enabler for next‑generation, data‑based techniques at Pet Centers. The role integrates animal testing techniques, analytical instrumentation, facility infrastructure upgrades, and robust data pipelines to ensure decision‑grade datasets are captured consistently and made available in end‑to‑end Facilities and R&D data systems.

What are we looking for?

Education

& Professional Qualifications Master’s degree (or equivalent experience) in analytical chemistry, chemical/process engineering, instrumentation engineering, food science, or a related scientific/engineering field. Formal training in laboratory safety and risk assessment; ability to operate within controlled environments (pilot plants, labs, Pet Centers).

Knowledge / Experience Hands-on experience deploying and validating analytical instrumentation (chromatography/spectrometry or comparable sensor technologies) in an applied R&D, pilot plant, or industrial environment. Experience writing SOPs, qualifying methods, and training operators/technicians; comfort translating complex technical requirements into practical routines.

Working knowledge of data integrity principles: metadata, file naming conventions, traceability, and basic data pipelines (preferred). Proven cross-functional project leadership across science, operations, engineering, and digital stakeholders; ability to influence without direct authority. What will be your key responsibilities?

Protocol harmonization & data interoperability: Lead trials program for the harmonization (or defined proof‑of‑parity where needed) of Pet Center techniques across Royal Canin facilities to enable consistent, comparable data capture and, where relevant, interoperability with the broader Petcare data ecosystem. This includes data capture for both dry and wet products to enable insights and interoperability across datasets and findings.

Method architecture & deployment: Select, specify, and deploy the on-site volatile & chemical non-targeted/fingerprinting capabilities (e.g., GC-IMS, ACP-MS, compact MS, ASAP) and define fit-for-purpose sampling approaches for wet and dry product matrices at key control points (pilot plant input and output; pre-trial intake at Pet Centers), co-developing sample preparation and analytical methods with external partners.

Support development of facilities infrastructure for advanced data capture of different type (from image/video capture and recognition, to wearables gadgets). Facility & utility readiness: Define and deliver the infrastructure prerequisites (space, utilities, ventilation, sample logistics, safety controls) for new instruments, including managing leasing/purchase and installation sub-projects; work with local engineering/maintenance in RLE and RCA to implement upgrades without disrupting daily operations.

SOPs, calibration & training: Create and maintain standard operating procedures, calibration/verification routines, and training materials; qualify users and ensure sustained method performance and data comparability over time. Protocol integration and “every meal matters” philosophy: Embed new data-capture steps into existing protocols (palatability, digestibility, RSS), research studies and feeding cycles to reduce cycle time and increase explanatory power without compromising animal welfare or study integrity.

Data pipeline to Monarch: Partner with Digital/Data teams and external vendors to structure metadata, automate ingestion, and enforce data-quality controls so instrument outputs and contextual parameters land reliably in the Monarch data lake. Global harmonization: Standardize the technical approach across Aimargues and Lewisburg (and extendable to other sites), aligning sampling, naming conventions, metadata, and quality checks so datasets are comparable and reusable for model training.

External collaboration & vendor management: Manage supplier interactions (rental/procurement, service contracts, SLAs), coordinate with laboratory partners, and ensure compliance with internal standards for safety, quality, and information security. Continuous improvement: Track method performance and operational adoption, identify bottlenecks, and implement improvements to increase throughput and reduce failure modes in the end-to-end measurement-to-data process.

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