What a data scientist does in F1
The F1 data scientist is responsible for extracting insights from the enormous amounts of data generated by an F1 car. Each car produces approximately 3 terabytes of data per race weekend — from telemetry sensors, tyre temperature monitors, GPS tracking, and aerodynamic measurements.
Their work spans the entire weekend: analyzing practice data to optimize setup, building predictive models for race strategy, and running real-time simulations during the race to inform pit stop decisions.
The machine learning revolution
Machine learning is transforming how F1 teams approach strategy. Algorithms can now predict tyre degradation with unprecedented accuracy, model the impact of weather changes on race outcomes, and simulate thousands of race scenarios in real time.
The best data scientists are the ones who can translate complex algorithmic outputs into simple, actionable recommendations for the race engineer and strategist. A model that predicts a 73% probability of rain in lap 32 is useless unless someone can decide whether to pit for intermediates.
The most influential data scientists
Hannah Schmitz (Red Bull) is one of the most visible data scientists in F1, making critical strategy calls that have won races and championships. Her ability to process real-time data and make the right call under pressure is unmatched.
Will Courteney (Mercedes) has been instrumental in Mercedes' strategic success, combining data science with race engineering to optimize every aspect of the team's performance.
The 2026 challenge
In the 2026 era, with Active Aero and complex energy management systems, the amount of data has increased exponentially. Data scientists must now model not just tyre degradation and fuel consumption, but also aero configuration, electrical energy deployment, and the interaction between all these variables.