Data Science Intern - Design of Experiments & Modelling

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About the job

Do you love analysis of different experimental problems ? Are you proactive and enjoying to interact with many teams ? Then apply now and join us as our new Data Science Intern - Design of Experiments & Modelling in our R&I office starting 1st of November 2026!

In this internship, you will join the Advanced Technology Process team, part of Danone's Global Research & Innovation organization. As our new Data Science Intern, you will investigate how Design of Experiments (DoE) and modelling-based approaches can support better experimental planning and more efficient decision-making in R&I. Through literature review, practical case examples and analysis of different experimental problems, you will evaluate the strengths, limitations and suitability of these approaches in an industrial context.

Your main responsibilities include:
* Review relevant literature on Design of Experiments (DoE) and modelling-based approaches for planning efficient experiments.
* Identify and categorize common experimental challenges in industrial R&I.
* Develop realistic mathematical models representing different types of experimental problems.
* Use these models as virtual experiments to evaluate different DoE and model-guided experimentation approaches.
* Compare their performance under conditions such as non-linearity, interactions, noise, constraints and limited experimental resources.
* Translate the findings into practical guidance for selecting an appropriate DoE or modelling-based experimentation approach.
* Document and present the results to relevant stakeholders.

Master's student in Data Science, Statistics, Applied Mathematics, Chemical Engineering, Process Engineering or a related field.
* You are highly motivated, enthusiastic and are not afraid of a challenge. 
* You have good communication skills and can work both independently and in a team.
* You are proactive and are not afraid of taking initiatives and bringing your own creative and innovative ideas to the team.
Here are some key requirements:
* Enrolled as a Master's student for the entire duration of the internship (if you are a non - EU citizen, you need to be enrolled at a Dutch university and your university must sign a three-party agreement with Danone).
* You are resident of the Netherlands with a valid BSN number. Non-EU citizens must have a valid Dutch residency permit.
* You are available to start in 1st of November for a minimum of 6 months, 5 days per week.
* You are able to work from our Utrecht office at least 2 days per week.
* You are fluent in English (spoken and written).
* You have knowledge of statistical modelling and Design of Experiments (DoE). 
* You are proficient in Python/R and have a solid grasp of Microsoft Office programs. " descriptionHeader="
Do you love analysis of different experimental problems ? Are you proactive and enjoying to interact with many teams ? Then apply now and join us as our new Data Science Intern - Design of Experiments & Modelling in our R&I office starting 1st of November 2026!

In this internship, you will join the Advanced Technology Process team, part of Danone's Global Research & Innovation organization. As our new Data Science Intern, you will investigate how Design of Experiments (DoE) and modelling-based approaches can support better experimental planning and more efficient decision-making in R&I. Through literature review, practical case examples and analysis of different experimental problems, you will evaluate the strengths, limitations and suitability of these approaches in an industrial context. 
 
Your main responsibilities include:
* Review relevant literature on Design of Experiments (DoE) and modelling-based approaches for planning efficient experiments. 
* Identify and categorize common experimental challenges in industrial R&I.
* Develop realistic mathematical models representing different types of experimental problems.
* Use these models as virtual experiments to evaluate different DoE and model-guided experimentation approaches.
* Compare their performance under conditions such as non-linearity, interactions, noise, constraints and limited experimental resources.
* Translate the findings into practical guidance for selecting an appropriate DoE or modelling-based experimentation approach.
* Document and present the results to relevant stakeholders." jobDuration="6 months" workFromHome="Hybrid