Football Machine Learning Engineer

Your mission

• Development of modern data science and machine learning solutions in football
• Working in an agile, cloud-based team of data scientists, developers, and product managers
• Designing new KPIs using statistical models and machine learning – e.g., for Bundesliga Match Facts
• Building innovative models to automate and optimize processes
• Contributing own ideas to the development of new analytical products
• Responsibility for the entire machine learning workflow – from data ingestion to deployment in production
  
  

Your profile

• Degree in Computer Science, Mathematics, Data Science, or a comparable field
• At least 5 years of experience in data science and machine learning
• Solid knowledge of Python and common machine learning libraries (e.g., PyTorch and data processing libraries)
• Experience in developing, validating, and deploying ML models, ideally in production-ready environments
• Familiarity with cloud infrastructure, particularly AWS SageMaker
• Experience in model design/training as well as evaluation and validation on large datasets
• Excellent verbal and written English skills
  
  

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.