Job Title: Senior Scientist: Process Modelling and Interpretable Machine Learning
The Manufacturing Intelligence (MI) team at Pfizer's Global Technology & Engineering is responsible for developing and implementing advanced analytics, including AI/ML, soft sensors, advanced process control, and process condition monitoring solutions in support of manufacturing and future capabilities.
This role will involve developing and implementing advanced analytics, real-time soft sensors, machine learning, advanced process control, and IIoT solutions to achieve actionable insights and enable continued improvement for pharmaceutical manufacturing and quality operations.
Key Responsibilities:
* Contribute to high-impact projects that require data analytics, advanced modeling, and optimization expertise.
* Identify opportunities for applying Advanced Analytics, APC, AI, ML, and IIoT, and develop and deploy innovative solutions in a manufacturing environment.
* Drive the development of mathematical and machine learning models and support GMP implementation of such analytics solutions.
* Apply engineering principles, modeling tools, and experimental skills to improve process understanding and facilitate real-time process monitoring and control.
* Collaborate with cross-functional teams and stakeholders, effectively communicate progress, and drive project progress in a timely manner.
Basic Qualifications:
* A PhD degree in a relevant field is preferred.
* Expert-level knowledge in Python is required. Experience in R, Matlab, or JavaScript is a plus.
* Ability to perform data engineering on big data ranging from structured time-series datasets to unstructured data.
* Track record in applying data science and machine learning methodologies to generate insight and support decision making.
* Collaborative mindset with excellent oral and written communication skills.
* Knowledge of upstream and downstream Biopharmaceutical Manufacturing.
* Experience with Interpretable Machine Learning or Explainable AI.
Preferred Qualifications:
* Expertise in first principles, hybrid modeling, and practical process modeling.
* Experience in cloud-based code development and deployment environments.
* Familiarity with cloud computing data-warehouses and relational SQL databases.
* Hands-on experience in deep learning and LVM for real-time monitoring and anomaly detection.
* Data visualization and real-time GUI experience.
* Familiarity with feedback control algorithms and industrial automation platforms.