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Luo Automation

Data Scientist in high-tech scale-up

Luo Automation
Netherlands · Full-time · Entry

What we do 

At Luo, we build machines that automate quality inspections of medicine. In many pharmaceutical environments, people still spend hours manually checking products for particles, cosmetic defects, and packaging integrity issues. It's tedious and humans make mistakes. We build machines that do this job more accurately, faster, and consistently. 

The brains of our machines are the detection models: algorithms that decide in real time whether a product passes inspection. We've proven the technology works. Now we're scaling and that means more clients, more applications, higher accuracy. For that, we need someone who builds, improves, and pushes these models to the next level. 

The Role 

We are looking for a Data Scientist who enjoys developing and optimizing machine learning models for real-world applications. Not just running models and reporting results, but understanding what has impact and execute what must be done to make it better. Most of the time, that is more than just re-training.   

You'll work on detection models for particle contamination, cosmetic defects, and closure integrity problems. You stay on top of the latest developments in the field, translate them into concrete improvements, and ensure our models meet the strict requirements of the pharmaceutical industry. You work closely with Software, Quality Support, and Machine Vision teams. 

What you'll work on 

  • Staying up to date: Following the latest developments in detection models and AI, assessing what's relevant for our custom models, and translating this into concrete research actions. 
  • Building and improving detection models: Adapting existing models to new clients, improving detection, and qualifying models’ performance offline.  
  • Orchestrating data strategy: Managing and executing dataset annotation for model training and defining which data needs to be available to build and improve the best detection models.  

What we are looking for 

  • Strong ML Foundation: You have solid experience with Python and machine learning frameworks such as PyTorch or TensorFlow. 
  • Data Analysis: You have a strong focus on analyzing image data, understanding patterns, and extracting meaningful insights to improve model performance. Experience with medical or industrial datasets is a plus. 
  • Hands-On Experience: You are comfortable owning the full pipeline, including data collection, annotation, and running experiments independently when needed. 
  • Analytical and communicative: You can explain complex findings clearly to non-technical people. You understand how to tailor your message to your audience. 
  • Eye for quality: You recognize that in the pharmaceutical industry accuracy is critical and understand the real-world impact of your models, especially when it comes to patient safety. 
  • Direct and transparent: You communicate openly about challenges. If a model underperforms or data is insufficient, you raise your concerns early so we can solve them together. 

What it's like to work here 

  • Real impact: Your models run in real machines at real client sites. When you improve something, you directly see the effect! 
  • Flat structure, short lines. Good idea? Go for it. Our structure is simple to favor involvement, creativity, and innovation. 
  • Interesting challenges: Detection problems in the pharmaceutical industry are complex and demanding. You’ll work at the edge of technology, where quality standards are high. 
  • A solid team: You work alongside engineers, operational, and commercial teams who all share the same goal. Cooperation and teamwork are key to building and delivering machines that actually work. 

Ready to build detection models that make a difference? Send your CV and a short intro to [email protected] 

Key Skills

Ranked by relevance

machine learning python ai
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Posted
May 06, 2026
Type
Full-time
Level
Entry
Location
Delft

Industries

Robotics Engineering

Categories

Engineering Information Technology

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