Job Description
Cpl in partnership with our client Pfizer Grange Castle are currently recruiting for a Senior Scientist: Process Modelling and Interpretable Machine Learning
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
· Experience deploying Interpretable Machine Learning or Explainable AI and knowledge of Shapley values and plots.
· Demonstrated experience of storytelling with interpretability tools usable by technical experts and non-technical stakeholders
· Use of exploratory analysis tools for abstractions such as feature visualization and attribution that aid scientists in interpreting and explaining machine learning model results
Independent, self-motivated personality with excellent oral and written communication skills
Preferred Qualifications
· Expertise in first principles (thermodynamics, reaction modeling, heat transfer, mass transfer principles), hybrid modeling. 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: Flexible - USA