Manager - Manufacturing Intelligence
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Key details
Why this role is notable
Pfizer is building agentic AI, soft sensors, and advanced process control into its drug manufacturing operations, signaling that AI is moving from research and marketing into the physical production and quality systems that make medicines.
Job Description
Use Your Power for Purpose
At Pfizer, technology impacts everything we do. Our digital and 'data first' strategy focuses on implementing impactful and innovative technology solutions across all functions, from research to manufacturing. By digitizing drug discovery and development, identifying solutions, or making our work easier and faster, you will be making a difference to countless lives. Your dedication and hard work will be instrumental in helping Pfizer achieve new milestones and make a positive impact on patients worldwide.
Role overview
The Manufacturing Intelligence and Technology Transformation (MITT) team within Pfizer’s Global Technology, Engineering and Launch organisation develops and deploys advanced manufacturing capabilities across Pfizer Global Supply. These capabilities include artificial intelligence and machine learning, soft sensors, advanced process control and agentic AI solutions.
The Data Science Manager will develop and implement advanced AI and process-modelling solutions in complex pharmaceutical manufacturing environments. This individual contributor role will focus on large language models, agentic systems, real-time soft sensors and advanced process control to generate actionable insights and support continuous improvement across manufacturing and quality operations.
Responsibilities
- Contribute technical expertise to high-impact analytics, modelling and optimisation projects.
- Identify high-value applications for advanced analytics, advanced process control, AI, machine learning, large language models, agentic AI and the Industrial Internet of Things.
- Design and deploy fit-for-purpose solutions for pharmaceutical manufacturing environments.
- Develop mathematical models, machine-learning systems and LLM-enabled agentic workflows, and support their implementation in GMP environments.
- Apply engineering principles, modelling tools and experimental methods to data-rich laboratory, pilot and manufacturing systems.
- Improve process understanding and enable real-time process monitoring and control.
- Collaborate with cross-functional teams and key stakeholders, communicate progress clearly and drive projects to timely completion.
Basic qualifications
- Expert-level Python proficiency is a must
- Experience preparing and analysing large, real-world datasets, including structured time-series data containing thousands of variables is required
- Demonstrated experience applying AI methods—including LLMs, knowledge graphs and agentic architectures—to generate insights and support decision-making.
- Proven ability to develop autonomous agents or AI copilots for manufacturing or scientific workflows.
- Strong collaboration skills and experience working in diverse, cross-functional teams.
- Knowledge of pharmaceutical or biopharmaceutical manufacturing processes.
- Excellent written and verbal communication skills, with the ability to translate technical concepts into actionable insights for technical and non-technical audiences.
- Adherence to best practice software engineering principles eg PEP8
Preferred qualifications
- A PhD with at least 2-3 years experience or Masters with 5-6 years experience in engineering, mathematics, computer science, or related discipline.
- Expertise in first-principles modelling, including thermodynamics, reaction modelling and heat and mass transfer.
- Experience developing hybrid and practical process models for real-time applications.
- Experience with cloud-based development platforms such as AWS SageMaker.
- Familiarity with cloud data warehouses such as Snowflake or Redshift and relational databases using SQL.
- Hands-on experience with deep learning and latent variable models for time-series monitoring, anomaly detection, and automated root-cause analysis.
- Experience integrating LLMs into real-time decision-support systems or user-facing agentic applications.
- Proficiency with visualisation and dashboard tools such as Streamlit, Plotly or Spotfire.
Work Location Assignment: Hybrid
Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.
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