Pfizer

Manager - Manufacturing Intelligence

Posted Yesterday

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4 Locations

Remote or Hybrid

Senior level

Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical

We’re in relentless pursuit of breakthroughs that change patients’ lives.

The Role

Develop and deploy AI, machine-learning, process-modeling, LLM, and agentic solutions for pharmaceutical manufacturing. Build real-time soft sensors, advanced process controls, monitoring systems, and decision-support workflows in GMP environments. Analyze large time-series datasets, improve process understanding, and support continuous improvement across manufacturing and quality operations while collaborating with cross-functional stakeholders.

Summary Generated by Built In

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 .
Information & Business Tech
#BI-Hybrid

Skills Required

Expert-level Python proficiency
Experience preparing and analyzing large real-world datasets, including structured time-series data with thousands of variables
Experience applying AI methods, including LLMs, knowledge graphs, and agentic architectures, to generate insights and support decision-making
Experience developing 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, including translating technical concepts for technical and non-technical audiences
Adherence to software engineering best practices, including PEP8
PhD with 2–3 years of experience, or Master's degree with 5–6 years of experience, in engineering, mathematics, computer science, or a related discipline
Expertise in first-principles modeling, including thermodynamics, reaction modeling, 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 visualization and dashboard tools such as Streamlit, Plotly, or Spotfire

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The Company

HQ: New York, NY

121,990 Employees

Year Founded: 1848

What We Do

Our purpose ensures that patients remain at the center of all we do. We live our purpose by sourcing the best science in the world; partnering with others in the healthcare system to improve access to our medicines; using digital technologies to enhance our drug discovery and development, as well as patient outcomes; and leading the conversation to advocate for pro-innovation/pro-patient policies.

Why Work With Us

We are the inventors, the problem solvers, the big thinkers — those who surmount any hurdle to deliver breakthrough medicines to the people who are counting on them the most.