An excellent opportunity has arisen to join a world leading global organisation, our global FMCG client, the powerhouse behind brands such as Domestos, Cif, Sure and Persil have an opening for a Statistician for a 6 month contract.Â This post will be based from their prestigious Research & Development Facility in Port Sunlight, Wirral, paying between £35,615 to £39,573 per annum, pro rata.
Our client has extensive R&D facilities around the globe with a mission to build brands through world-class innovation by unlocking science and technology.
The Digital R&D team is leading this transformation of R&D through programs in Big Data Analytics, Predictive modelling and Product Life Cycle Management with a view to driving speed, efficiency and connectivity of R&D. The Modelling & Analytics team in Digital R&D, provides leading edge statistics, data science, data management, modelling and simulation expertise to deliver predictive models and advanced analytics for new insights to the category programs across all R&D functions.
The focus of the Data Science team is in the design and planning of experiments, analyses of data, building and validating predictive models and interpretation and exploitation of outcomes together with the project team.
The products and brands we support are across categories in Foods & Refreshment, Home Care and Beauty & Personal Care.
We are looking for a Data Scientist/Statistician to support product innovation and science and technology teams in Home Care and Beauty and Person Care Divisions. You must have a strong background in design of experiments and modelling product formulation and processing to gain understanding and ultimately predictive models to sit on the virtual product simulation suite. You must be comfortable working with a wide range of stakeholders and functional teams.
Engagement and collaboration with multi-disciplinary teams to design and implement efficient, well-focused, statistically fit-for-purpose studies. Build and validate predictive models and simulations.
Responsibility for the quality and defensibility of statistical analyses, models and data interpretation.
Experience in some of the following areas is desirable but not essential:
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