Senior Scientist Process Modeling

at  Fastnet Recruitment

GRANGE CASTLE

Our client is looking for someone to join their Manufacturing Intelligence team. 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.

REQUIREMENTS:

• 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.
• Knowledge of upstream and downstream Biopharmaceutical Manufacturing
• Expertise in first principles (thermodynamics, reaction modeling, heat transfer, mass transfer principles, cell growth), hybrid modeling. Ability to develop practical process models for real-time applications is a strong plus.
• Familiarity with feedback control algorithms, real-time communication protocols, industrial process historians, and industrial automation platforms such as DeltaV and ASPEN.

• Technical contribution to high-impact projects that require data analytics, advanced modeling, 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, modeling 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.