Internship – Data Scientist / Machine Learning (Football Analytics)

Your mission

• Support the development of modern data science and machine learning solutions in football
• Collaborate in an agile, cloud-based team of data scientists, developers, and product managers
• Contribute to the design of new KPIs and models for applications such as Bundesliga Match Facts
• Analyze and prepare large-scale football data sets (e.g., tracking, event, and positional data)
• Assist in building models to automate and improve data-driven processes
• Support the end-to-end machine learning workflow – from data ingestion and feature engineering to model evaluation
  
  

Your profile

• Currently enrolled in or recently graduated from a degree program in Data Science, Computer Science, Mathematics, Statistics, or a related field
• First hands-on experience with Python and common libraries (e.g., NumPy, Pandas, scikit-learn, PyTorch)
• Strong interest in machine learning anddata analysis
• Passion for football and data
• A structured, proactive working style and the ability to work in a team
• Good verbal and written communication skills in English
  
  

About Us

At Sportec Solutions, a joint venture between Deltatre and the DFL Group based in Munich, we develop innovative solutions in sports data and technology. Since our founding in 2016, we have been committed to revolutionizing the use of data in Soccer and helping sports organizations worldwide unlock the full potential of their data ecosystems.

Our offerings include cutting-edge technology for the collection, storage, analysis, and delivery of live match data. We also provide official services such as goal-line technology and centralized support for the Video Assistant Referee. Through close collaboration with leading companies and academic institutions, we continuously advance the development of our products.

We are committed to fostering a diverse and inclusive work environment, promoting equal opportunities for all employees. For better readability, only the masculine form is used, but we naturally address all genders equally.