Would you like to use data analytics to understand what affects lead times in pharmaceutical manufacturing? In this thesis project, you will analyze large volumes of batch data to identify bottlenecks and process variation, and develop insights to support production improvements.

About AstraZeneca:

AstraZeneca is a global, science-led, patient-centered biopharmaceutical company focusing on discovering, developing, and commercializing prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit.

About the Opportunity:

As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business.

Thesis work description:

A new control system has been introduced in production, and batch lead times are reported to be longer than with the previous system. In this thesis project, you will analyse batch execution data collected by the DeltaV process control system to investigate where delays and waiting times occur, what may be causing them, and where there is potential to improve overall lead times.

You will develop a repeatable method for extracting, processing and analysing the available data, using Python or similar tools. The data may include information on process execution, equipment utilisation, waiting times, alarms, operator interventions and batch durations. Working with process engineers, operators and the automation team, you will validate your findings and assess which improvement opportunities could have the greatest impact.

Key Objectives:

• Identifying bottlenecks, waiting times and process variations that may contribute to longer batch lead times.
• Quantifying the potential impact of identified improvement opportunities on manufacturing performance.
• Developing visualisations and KPIs to support ongoing process monitoring and optimisation.
• Exploring how data analytics and AI-based methods could support future lead-time improvements.
• The expected outcome is a set of process improvement recommendations, a repeatable analysis methodology and a proof of concept—such as a dashboard or analytical framework—for identifying opportunities to reduce lead times.

Placement:  

This is an on-site position at AstraZeneca Södertälje.

AstraZeneca does not support with accommodations for this role

Structure:

• Duration: Spring 2027
• Credits: 15
• 1-2 students

Essential Requirements:

• Currently studying for a bachelor’s degree in a relevant field, such as chemical engineering, process engineering, industrial engineering, automation and control, data science, computer science, applied mathematics or statistics.
• Experience with data analysis and basic statistical methods.
• Programming skills in Python, R or a similar language.
• Knowledge of data processing; experience with databases or SQL is an advantage.
• Strong analytical and problem-solving skills, with the ability to communicate findings clearly and systematically.
• Interest in manufacturing processes, industrial automation and digitalisation.

Desirable Requirements:

• Experience analysing process, production or time-series data.
• Knowledge of process control systems or pharmaceutical manufacturing.
• Experience with DeltaV.
• Familiarity with data visualisation tools such as Power BI.
• Knowledge of machine learning or AI techniques.

So, what’s next?

Apply today and take the chance to be part of making a difference, making connections, and gaining the tools and experience to open doors and fulfil your potential. We can´t wait to hear from you!

We welcome your application as soon as possible, but ahead of the scheduled closing date November 10, 2026. In the event that we identify suitable candidates ahead of the scheduled closing date, we reserve the right to withdraw the vacancy earlier than published.

Date Posted

Closing Date