Senior Scientist – Data Science & Chemometrics

About the role

The Senior Scientist – Data Science & Chemometrics will apply advanced statistical analysis, chemometrics and machine‑learning techniques to complex analytical, sensory and process data across Pernod Ricard’s global spirits portfolio. Working closely with analytical, sensory, research and digital teams, you will create data‑driven models that link chemical composition, production parameters and consumer perception to improve product quality and accelerate innovation.

Key responsibilities

• Develop, validate and deploy chemometric, statistical and machine‑learning models to extract insights from analytical, sensory, process and consumer datasets.
• Build predictive and explanatory models that connect chemical composition, maturation conditions and sensory attributes to quality, stability and consumer preference.
• Apply multivariate techniques such as PCA, PLS, cluster analysis, Design of Experiments, mixed models, regression and classification, including model validation.
• Use statistical process control and multivariate data analysis to monitor and optimise manufacturing processes.
• Identify chemical, process and sensory drivers of product differentiation, authenticity and consumer‑relevant characteristics.
• Act as the subject‑matter expert for chemometrics and advanced analytics, providing scientific guidance to multidisciplinary project teams.
• Integrate sensory, analytical, process and consumer data into structured insight frameworks to support evidence‑based decision‑making.

Required profile

• PhD or MSc in Chemistry, Data Science, Statistics, Food Science or a related discipline.
• Proven experience applying chemometrics and advanced data‑analytics in a research or industrial setting, preferably within food, beverage or spirits.
• Strong analytical mindset with the ability to translate complex data into actionable insights.
• Excellent communication skills and experience working collaboratively across cross‑functional teams.

Required skills

• Chemometrics
• Machine learning
• Statistical modelling
• Multivariate analysis (PCA, PLS, cluster analysis, DOE, mixed models, regression, classification)
• Statistical process control
• Predictive modelling