Description
Imagine a future where scientific data flows seamlessly across Analytical Development, insights are available at the point of need, and digital technologies amplify the impact of our scientists.
In Drug Development & Supply, this future is being driven through CMC Digitalisation, a transformation focused on how we generate, connect and use data across the development lifecycle.
The Principal Scientist – AD Business Process Specialist plays a key role in this transformation across the CMC portfolio, leading digital use cases that bring the vision to life.
In this role you will…
- Partner with the Business Process Owners and AD leadership to support the digital roadmap, representing Analytical Development in CMC digital transformation programmes and ensuring your use cases align to strategic priorities and future-state operating models.
- Translate scientific workflows, business challenges and user needs into clearly defined digital use cases, business requirements, success criteria and definition of done, working alongside scientists, process owners, architects, developers and data specialists.
- Lead your portfolio of digital use cases end to end, from concept to deployment – building plans, milestones and resource requirements, and leading cross-functional project teams through prioritisation, issue resolution and decision making.
- Lead business acceptance testing and pilots, coordinating subject matter experts and user feedback to ensure solutions are validated and fit for purpose before go-live.
- Drive adoption of new digital tools through structured change management, user engagement, training and communication plans that maximise uptake and realised value.
- Build digital and data literacy across AD teams, championing user-centric design and data-driven ways of working.
- Promote the effective use of analytical and scientific data to improve decision making and performance, working with data governance and analytics teams to improve data quality, accessibility, usability and integrity.
- Partner with platform and support teams to deploy new capabilities, ensure organisational readiness and business continuity, and use performance monitoring and user insight to drive continuous improvement.
Essential Requirements:
- BSc, MSc or equivalent in a scientific or technical discipline.
- Strong knowledge of CMC and analytical development, with experience supporting the development and lifecycle management of pharmaceutical products.
- Proven track record of driving process transformation through the implementation of innovative ways of working, digitalisation, and the deployment of new capabilities, including automation, modelling, and other technology-enabled solutions to improve efficiency, quality, and business outcomes.
- Evidence of strong critical thinking, sound judgment, and problem-solving skills, coupled with a high degree of learning agility and adaptability when navigating ambiguity and change.
- Excellent written and oral communication skills and the ability to interact effectively with scientists across disciplines and key stakeholders with a collaborative attitude.
- Demonstrated ability to contribute effectively within cross-functional, matrixed environments, with experience leading and coordinating small sub-teams to deliver shared objectives, influence stakeholders, and drive outcomes through collaboration.
Preferred Requirements:
- Experience leading or supporting organisational change initiatives, including stakeholder engagement, change adoption, and embedding new ways of working across teams or functions.
- Experience using and implementing digital tools and data-driven solutions to enhance scientific decision-making, process efficiency, and business outcomes (e.g., data visualisation, statistical analysis, modelling, automation, artificial intelligence/machine learning, laboratory informatics, or digital workflow platforms).
- Understanding of GxP data integrity principles and requirements, with experience ensuring the reliability, accuracy, traceability, and governance of scientific and operational data.