For many fans of Formula One, the sport exists between lights and chequered flag on a Sunday afternoon. It begins and ends with the exploits of the drivers on the track. But this is merely the tip of the spear. The reality of modern F1 is that of a complex and intertwined operation, every part of which needs to perform near its limit if success is to be achieved. From the pit crew searching for the ultimate repeatable pit stop, to the inspiration of the designers, the application of engineers and the herculean efforts of an army of fabricators and machinists. Much of our success is thanks to the diversity of thought and spectrum of skill sets held within the team, our ability to recognise unique contributions from individual team members is just a part of whywe love what we do.
Job Description
We are looking for a highly motivated and experienced Data Scientist to lead the Machine Learning and Data Science Team at Red Bull Technology and Red Bull Racing, tasked with contributing state-of-the-art Machine Learning techniques to improve overall car performance. You’ll be managing a small team of Data Scientists and have the opportunity collaborate with software engineers and domain specialists in the various technical departments in the wider organisation.
Working within the Technology & Analysis Tools department from our Milton Keynes campus, our mission is to develop the technology that enables our engineers to design championship winning Formula 1 cars. This is a unique opportunity to make a significant contribution to technology that will define the way we exploit car performance in the future.
The impact you will have
Lead the development of Machine Learning and Data Science tools that improve our understanding of vehicle performance and design
Line-manage a team of Data Scientists, contributing to the growth and strategy of the team as well as overseeing recruitment
Collaborate with senior members of Vehicle Performance, Aerodynamics, Vehicle Design and Power Unit departments to understand how Data Science can enhance existing practices and address challenging problems
Work alongside a team of highly skilled software engineers to create the necessary infrastructure to productionise models
Lead the introduction of state-of-art literature and research from the Machine Learning community into the team’s activities
Present work with effective communication, writing and visualisation to a wide range of stakeholders
Lead others in the wider Technology department by example with best practices in analysis, programming, and communication
What we look for
You hold a postgraduate degree (e.g. MSc or PhD) in Mathematics, Statistics, or a similar quantitative field
You have preferably 5 years industry experience as a data scientist (or the equivalent in a post-Doctoral/research position), and experience mentoring/managing other Data Scientists
You are a technical expert and can successfully balance the demands of individually contributing to the team’s modelling assets whilst managing a team
You have a track record of utilising a wide range of Machine Learning and Data Science techniques, as well as knowledge of the underlying mathematics of them, e.g. Bayesian Statistics, Deep Learning and Optimization
Proficient in Python and experience with using popular Machine Learning frameworks/libraries such as TensorFlow and PyTorch
Excellent communication skills and a track record of collaborating successfully with stakeholders from other disciplines
A robust understanding of containerisation and orchestration frameworks like Docker and Kubernetes, as well as a track record of productionising models
Bonus points for
Not only is this a fantastic role, but it is also a fantastic team to work with here at Red Bull Technology. A good salary is just the start, there are many other benefits too such as our bonus scheme, private health care cover, life assurance scheme, workplace nursery, company contributed pension scheme, on site gym & fitness classes, free food, and a cycle to work scheme.
Job Posting End DateTue, 25 Mar 2025#J-18808-Ljbffr
