Data Science Engineer
Permanent - Full TimeJob Overview
Are you passionate about applying advanced modelling and AI to unlock performance in one of the most cutting edge engineering environments in the world? Join our Vehicle Science team as a Data Science Engineer, where your work will directly influence and continuously improve the performance of our Formula One car.
This is a unique opportunity to use mathematical modelling, physics based methods, and machine learning to accelerate simulation, improve predictive capabilities, and generate high value insights across aerodynamics and other engineering domains.
Key Responsibilities
In this role, you'll be at the forefront of data driven performance engineering. You will:
- Build robust, reliable data driven and physics informed models using advanced mathematical and AI/ML methods.
- Develop and optimise scalable data pipelines integrating simulation data, physical testing outputs, and trackside measurements.
- Research and implement state-of-the-art AI/ML techniques to improve modelling fidelity and computational speed.
- Collaborate with Aerodynamics, Vehicle Performance, Simulation & Modelling and other technical groups to embed insights into engineering decisions.
- Communicate findings through clear reports, visualisations, and presentations for both technical and nontechnical audiences.
- Ensure data quality, security, and compliance across the modelling workflow.
- Write clean, maintainable code using modern software engineering practices and AI assisted development tools.
Skills, Knowledge and Expertise
We’re looking for someone analytical, curious, and ready to push boundaries. You should have:
- A master's degree or higher in Mathematics, Physics, Engineering, Computer Science or a related field.
- Strong understanding of reduced order modelling and ideally exposure to fluid mechanics or other complex physical systems.
- Excellent analytical skills across experimental methods, modelling, statistical inference, and data driven techniques.
- Familiarity with surrogate modelling, emulators, and predictive algorithms used to accelerate engineering workflows.
- Preferably, strong programming skills in Python and experience with ML/scientific libraries such as SciKitLearn, JAX or PyTorch. However, software training will also be provided.
- Ability to work calmly under pressure, manage competing priorities, and deliver high-quality results on tight timelines.
- Strong problem-solving ability and confidence making data driven recommendations.
- A collaborative mindset and enthusiasm for building strong working relationships across teams.