Chemometrician / Data Scientist

Key Skills

Python Programming, MATLAB Programming, Data Analytics

Job Description

The NPI (New Product Introduction) and Technical function provides best in class processes that ensure all product inputs are understood and maintained from R&D development throughout the product life-cycle within the wider manufacturing and production operations across our Ware OnePharma Campus (R&D + Manufacturing). This entails all elements of the tech-transfer, clinical and commercial manufacture, testing and supply process from launch through to planned changes and to transfers.

As a Data Scientist / Chemometrician within our PAT & Data Analytics Team, you will be involved in a variety of data modelling activities that support R&D Late Stage Development, New Product Introduction (NPI) and Global Supply Products (GSP) across the Ware campus. Your main responsibilities will include the development and implementation of chemometrics and data analytics models to accelerate process development, enhance process understanding, and perform real-time process & quality monitoring. In conducting your role, you will work closely with R&D Scientists, Formulators, Process Technologists and PAT Engineers that will help you to contextualize your modelling activities. You will also have the chance to familiarize with the data that you will analyze by participating and supporting PAT deployment during developmental studies, clinical and commercial campaigns.

PLEASE NOTE: Due to the breadth of services that the Team provides to the site we are able to consider suitably qualified recent graduates as well as more experienced professionals. We are interested in applicants with qualifications / education / experience in Chemometrics, Spectroscopic Techniques and Pharmaceutical Manufacturing Process.

About You:

You will have a suitable Scientific or Engineering degree. Ideally, you will have experience of the application of PAT & Data Analytics solutions for pharmaceutical products, but we are open-minded about your background experience. The most important elements are that you should be a team player; with a hands-on, responsible and compliant approach to Quality, Safety, EHS and cGMP. You should have a passion for data and the use of this to improve our understanding of process and product performance. Experience of work with cross-functional teams would be beneficial, and you will be self-motivated and have good communication skills.

During the application process we are looking to see that applicants have a some understanding of the complexities and importance of supporting production within a pharmaceutical manufacturing process.

Basic Qualifications:

• Degree in data science or engineering or related subject.
• Excellent learning agility and good communication skills.
• Demonstrated ability to deploy Data Analytics solutions, with understanding of mainstream chemometric techniques.
• Demonstrated understanding of requirements for pharmaceutical processes development and industrialization.
• Demonstrated understanding data and informatics requirements for digital platforms.

Key Responsibilities

• In liaison with R&D chemometricians, define requirement for the deployment of PAT/data analytics/chemometric models in the manufacturing environment.
• In liaison with other functions (e.g. process automation, manufacturing technologists) execute to the deployment of chemometric models in the manufacturing environment.
• In liaison with other functions (e.g. quality) contribute to the definition of processes, guidance and procedures for chemometric models maintenance and review across the product lifecycle.
• Translate chemometric models using relevant software platforms (e.g. SIPAT, SIMCA, Aspentech Unscrambler) or by writing custom programs (Using Matlab, Python, etc.).
• Support the integration of chemometric modeling with other data science disciplines and platforms (e.g. informatics and automation) to maximize opportunities to accelerate the portfolio and enhance process robustness.
• Stay current with new chemometric and other data science modeling techniques through publications, conferences and academic and precompetitive collaborations.
• Coordinate with other SMEs across sites to share best practices for models deployment and maintenance.