- Cpl in partnership with our client Pfizer Grange Castle are currently recruiting for a
Senior Scientist: Process Modelling and Hybrid Modelling
The Manufacturing Intelligence (MI) team within Pfizer’s Global Technology & Engineering (GT&E) is responsible for driving the development and implementation of advanced analytics including AI/ML, soft sensor, advanced process control, and process condition monitoring solutions in support of manufacturing and future capabilities in Pfizer Global Supply (PGS).
As a member of MI, this role will have the opportunity to develop and implement advanced analytics, real-time soft sensors, machine learning, advanced process control and IIoT solutions/capabilities in manufacturing settings to achieve actionable insights and enable continued improvement for pharmaceutical manufacturing and quality operations.
Responsibilities:
- Technical contribution to high-impact projects that require data analytics, advanced modelling, and optimization expertise.
- Identify high value opportunities for applying Advanced Analytics, Advanced Process Control (APC), Artificial Intelligence (AI), Machine Learning (ML) and Industrial Internet of Things (IIoT), and develop and deploy innovative fit-for-purpose solutions in manufacturing environment
- Drive development of mathematical and machine learning models and support GMP implementation of such analytics solutions
- Apply engineering principles, modelling tools, and experimental skills using data-rich lab/pilot/manufacturing equipment to improve process understanding and facilitate real-time process monitoring and control
- Collaborate with cross-functional teams and key stakeholders, effectively communicate progress to management, and drive project progress in a timely manner.
Basic Qualifications:
- A PhD degree in relevant engineering major, mathematics, or computer science is preferred.
- Expert-level knowledge in Python is a must. Experience in any of the following languages is a plus: R, Matlab, JavaScript.
- Ability to perform data engineering on real world big-data ranging from structured time-series datasets with thousands of features, to unstructured image, text, audio and video data.
- Track record in applying data science and machine learning methodologies to real-world data to generate insight and support decision-making.
- Ability to work collaboratively in diverse cross-functional teams on innovative solutions and tools with an open attitude towards fast learning
- Knowledge of upstream and downstream Biopharmaceutical Manufacturing
- Expertise in first principles (thermodynamics, reaction modelling, heat transfer, mass transfer principles, cell growth), hybrid modelling. Ability to develop practical process models for real-time applications is a strong plus.
- Demonstrated experience of story-telling to technical experts and non-technical stakeholders
- Familiarity with feedback control algorithms, real-time communication protocols, industrial process historians, and industrial automation platforms such as DeltaV and ASPEN.
- Independent, self-motivated personality with excellent oral and written communication skills
Preferred Qualifications:
- Expertise in first principles (thermodynamics, reaction modelling, heat transfer, mass transfer principles), hybrid modelling. Ability to develop practical process models for real-time applications is a strong plus.
- Experience in cloud-based code development and deployment environments such as AWS SageMaker or Tibco.
- Familiarity with cloud computing based data-warehouses such as Snowflake or Redshift, and relational SQL databases.
- Hands-on experience in deep learning and LVM for real-time monitoring and anomaly detection of time-series data and automated root cause analysis.
- Experience in data visualization and real-time GUIs using Streamlit, Plotly, Spotfire, etc.
- Familiarity with feedback control algorithms, real-time communication protocols, industrial process historians, and industrial automation platforms such as DeltaV and ASPEN.
- Knowledge of Cell Culture, Fermentation and Vaccines Conjugation
- Work Location Assignment: Hybrid, Flexible - dealing with USA