Data Scientist On-Site Presence

Full Time

Permanent contract

As a Data Scientist at Swiss Timing, you will play a key role in transforming raw sports data into meaningful insights that support performance analysis, data-driven decision-making, and data storytelling. Working as a core member of an interdisciplinary tech team, your primary mission is to develop advanced machine learning models, agentic systems, and AI-powered self-service analytics solutions that drive our live and post-processing systems across a wide range of sports.

You will collaborate closely with data engineers, software developers, and sports domain experts to build robust analytics pipelines and AI-powered systems. This role requires strong scientific thinking, hands-on ML development, and a collaborative mindset to deliver reliable, sport-specific performance metrics.

• Apply statistical analysis to complex datasets and develop, evaluate, and continuously improve supervised and unsupervised machine learning models
• Develop agentic systems that interact with data sources and analytical tools
• Build AI-powered self-service analytics solutions to create tailored analyses, visualizations, and actionable insights for athletes, federations, media, and internal stakeholders
• Analyze and contextualize sports data to develop algorithms that compute key performance metrics
• Partner with engineering teams to ensure databases used for ML and AI applications are clean, structured, and validated with robust data quality checks
• Work closely with our technical and engineering teams to deploy, maintain, and monitor machine learning models in production
• Contribute to innovation by exploring new methodologies and proactively improving existing AI and ML systems

• Strong analytical and conceptual thinking skills
• High affinity for machine learning, generative AI, data-driven problem solving, and complex systems
• Structured, independent, and solution-oriented working style
• Ability to communicate complex data and insights in a clear and understandable way
• Interest in sports and data-driven performance analysis
• Detail-oriented and quality-focused
• Team player with the ability to collaborate effectively in interdisciplinary environments
• Curiosity and motivation to continuously learn and explore new technologies and methods

• Degree in Data Science, Computer Science, Engineering, Mathematics, or a related field
• Strong background in statistics and machine learning, including experience with supervised and unsupervised learning, and model evaluation.
• Experience with LLM-based applications or agentic systems
• Proficiency in Python; experience with SQL, C++, and C# is a plus
• Experience with data visualization, scientific analysis workflows, and deploying models into production environments
• Solid understanding of scientific methodology, testing, and experimental validation

• Good communication skills in English are required.