Senior Data Engineer

At GSK, we manufacture and supply reliable, high-quality medicines and vaccines to meet patients’ needs and drive our performance. Our network of 37 medicines and vaccines manufacturing sites delivered 1.7 billion packs of medicines and 409 million vaccine doses in 2024 to help make a positive impact on the health of millions of people. Our supply chain is not just core to our operations; it’s vital to bringing our innovations to patients as quickly, efficiently and effectively as possible. Technology is transforming how we manufacture medicines and vaccines, enabling us to increase the speed, quality and scale of product supply. We need the very best minds and capability to help us on our journey to make more complex products, harnessing the power of smart manufacturing technologies including robotics, digital solutions and artificial intelligence to deliver for patients.

The Manufacturing Science and Technology (MSAT) organization is a site-based technical team responsible for ensuring manufacturing processes are capable, compliant and productive while maintaining product quality.  The site MSAT team is also responsible for delivering continuous improvements to the existing supply chains supported by UM Biopharm and working with global MSAT to execute technology transfers and implement process-related improvement programs across the product lifecycle. The Upper Merion MSAT team is also accountable for delivering data science and analytics solutions to improve business outcomes.

The Senior Data Engineer deploys data science and data analytics principles to provide business insights and solve complex technical and business problems. The individual will use data as a key asset to accelerate technology transfer and CMC, drive continuous improvement in key business processes, reduce waste, and enhance knowledge management across the site. Individual may work with a number of stakeholders across site including manufacturing and technology groups involved in monitoring (CPV), comparability, and investigations, analytical teams involved in product, release, and stability testing, quality teams engaged in managing quality systems and quality data, and engineering groups responsible for process control, reliability, and robustness to build informatics solutions that meet the desired customer needs and user experiences. The individual will deploy novel digital models, tools, and visualizations and  be responsible for maintaining site databases, models, and infrastructure.

This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:

• Leverage digital tools and solutions to drive business value and continuous improvement in technical or site systems and processes, including CMC (new product introduction and regulatory filing), technical (process monitoring and CPV, comparability, and investigations), site sustainability, manufacturing plant (engineering) performance and reliability, quality control release and stability data, and quality systems and data.
• Maintain site-based digital infrastructure, including models, databases, and software.
• Manage relationships with key internal (i.e. digital, data, analytics, production, engineering, quality) and external (i.e. central and external MSAT teams, tech, R&D) partners to deliver technical objectives and implementation of novel data and analytics platforms and technologies.

Why You?
Basic Qualifications:
We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

• Bachelors with a minimum of 5 years OR a Masters (MS) degree with a minimum of 3 years OR PhD with minimum of 3 years of post-graduate experience in data engineering, data science, computer science field.
• Experience in digital, data, and analytics techniques in a highly regulated environment.

Preferred Qualifications:
If you have the following characteristics, it would be a plus:

• Experience in biopharmaceutical development and/or manufacturing
• In-depth experience with digital, data, and analytics techniques. Familiarity and experience with relational databases, statistical software (i.e. SAS, SIMCA, JMP or WebStatistica), modeling and visualization software (i.e. Python, R, Matlab, , SQL, MS Power).
• Experience of developing or applying techniques such as pre-processing, classification, regression, clustering, dimensionality reduction and model selection.
• Experience with development and maintenance of digital infrastructure, including cloud architecture.
• Experience with manufacturing / engineering environments including systems such as IP-21, M-ERP, LIMS, Aspen eBRs, is preferred.