Data Scientist KTP Associate (Fixed Term)
Brighton £47,389 - £56,534
Job sector
Digital and Technology
Job function
Data Science
Job duration
30 months
Application closing date
28/05/2026
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Job description
A key part of the role is to consolidate the modelling and optimisation capability into outputs that demonstrate value to clients and support the first commercial launch of this new service. The post holder will lead day-to-day project activity, report to joint University and company governance structures, and contribute to the transfer of knowledge between academic and industrial partners. The role is expected to support CP’s longer-term strategy by embedding sustainable capability and enabling the development of new data-driven services.
Project description
Working closely with academic supervisors at the University of Sussex and multidisciplinary teams across CP, the post holder will follow CP’s New Product Introduction process to review available datasets, assess current analytical capability, and design predictive models to support formulation and process decisions. These models will be applied to optimise development activities and embedded into client-facing workflows.
About the business
CP is a UK-based contract development and manufacturing organisation supporting clients in bringing new medicines to market. The Knowledge Transfer Partnership (KTP) will support CP in establishing new in-house capability in data analytics and quantitative modelling, enabling more systematic and reliable decision-making across drug product development activities. The role sits at the interface between the company and the University and is central to delivering the objectives of the partnership.
At present, many development decisions rely on expert judgement and manual processes, despite the availability of large volumes of process and formulation data. The project will focus on developing and applying advanced analytical and predictive modelling approaches to improve how this data is analysed and interpreted. Initial work will focus on early-stage product development case studies, with the aim of reducing trial-and-error activity, improving development success rates, and shortening development timelines.