Data Scientist (all genders) - On-Site Presence

Swiss Timing LTD; Bern; Vor Ort; CHF 110.000 - 140.000; Vollzeit

Zusammenfassung

Swiss Timing LTD is seeking a Data Scientist (all genders) to transform raw sports data into meaningful insights for performance analysis and data storytelling. This on-site role focuses on developing ML models and AI-powered analytics across live and post-processing systems for various sports.

Collaborate with data engineers, software developers, and sports experts to build analytics pipelines, deploy models into production, and ensure data quality and robust validation.

Qualifikationen

• Degree in Data Science, Computer Science, Engineering, Mathematics, or a related field.
• Strong background in statistics and machine learning, including supervised and unsupervised learning and model evaluation.

Aufgaben

• Apply statistical analysis to complex datasets and develop, evaluate, and improve supervised and unsupervised ML models.
• Develop agentic systems that interact with data sources and analytical tools.
• Build AI-powered self-service analytics solutions for tailored analyses, visualizations, and insights for athletes, federations, media, and internal stakeholders.
• Analyze and contextualize sports data to develop algorithms computing key performance metrics.
• Partner with engineering teams to ensure data used for ML/AI is clean, structured, and quality checked.
• Work with engineering teams to deploy, maintain, and monitor ML models in production.
• Contribute to innovation by exploring new methodologies and improving AI/ML systems.

Kenntnisse

Analytics mindset

ML enthusiasm

Independent thinker

Clear communicator

Sports interest

Detail oriented

Team player

Curious learner

Ausbildung

Degree in Data Science or related field

Tools

Python

SQL

C++

C#

Jobbeschreibung

Data Scientist (all genders) - 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

Profile

• 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

Professional requirements

• 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

Languages

• Good communication skills in English are required.

Location