About this role
Are you a Msc data science student who is looking for an internship assignment? Are you ready to commit your data analytical skills to improve our technical insights? We are looking for you!
Your assignment Canon Production Printing (CPP) develops and manufactures commercial printers for the business-to-business market. Our inkjet printers combine high-speed, high-volume printing with low operating costs, high quality and flexibility. One of our printers handles large volumes of varied paper at very high speeds. Numerous sensors and control loops regulate the sheets movement through the printer. When paper jams, the obvious response is to remove it and resume printing.
However, most jams stem from deviations in paper/ sheet movement and position upstream of the jam. To diagnose the problem the paper path is equipped with multiple sensors, but identifying the root cause is difficult for service technicians because sensor data is complex to interpret: a sheet passes some sensors multiple times, and the sensors measure different aspects of its position, like the front edge and back edge of the sheets or skewness.
This project aims to develop a model that helps service technicians pinpoint the cause of paper jams by narrowing their diagnostic focus or even providing the root-cause. It will require analysis of all available digital information of failed, but also, good print jobs of thousands of sheets. Can you create a reliable model that makes paper-jam diagnosis easy for technicians of all experience levels?
Challenges for you are
- Get to know the data set and organize to enable fast and reproducible data analysis
- Use advanced data analytical techniques like unsupervised clustering to create a model that reacts on different error-codes and pinpoints to the root cause within a failed print job.
- Visualize your results in a clear way so all levels of technicians can understand and act upon it.
- Dealing with large amounts of data. Our printers log a lot of detailed information. It is up to you to aggregate or filter on the right level. You’ll do this based on discussions with domain experts.
Your profile
You are currently studying Data Science or Data Analytics and you are looking for your MSc internship or graduation assignment at a big international company You have a critical attitude, and you are curious to understand your own observations. You have at least basic Python and SQL knowledge As of December 1st 2026 you are available for 5 to 8 months for a minimum of 4 days per week What’s in it for you?
- A challenging assignment with skilled coaching
- Internship/ Graduation compensation up to €500,- per month
- Travel cost compensation if you don’t have an ‘OV-weekcard’
- The possibility to network with professionals inside and outside your field of expertise, thanks to our diversity of disciplines which you will work with
- With our company values collaboration, innovation and ownership, we strive to bring out the best in each other, expand boundaries and feel empowered to take ownership of our work.
Interested?
Are you interested in this assignment? Please click on the button 'apply now' where you can upload your resume and motivation letter. If you would like to receive more information concerning this assignment, please contact Pawel Oortwijn, tel. +31 (0)77 359 3276.
If you have any questions about the internship in general, please contact [email protected] What do we stand for? We develop and manufacture high-tech printing products and workflow software for the commercial printing market as part of Canon, a global leader in imaging technologies. With around 3,300 employees across three continents and our headquarters in Venlo, the Netherlands, we innovate to create high-quality solutions that add color to the world. Guided by the philosophy of Kyosei —living and working together for the common good—our culture is built on openness, collegiality, trust and stability. We empower our people to grow, take initiative, and make an impact. Further information about Canon Production Printing is available at cpp.canon . #LI-DNI