PU Data Scientist

Red Bull; England; On-site; GBP 50,000 - 70,000; Full time

Job summary

A leading motorsport technology firm in the UK seeks a PU Data Scientist to drive data strategy and insights within their engineering team. You will analyze PU data and collaborate with engineers to enhance performance. The role requires a strong background in data analysis, machine learning, and a passion for motorsports. Excellent benefits included such as bonuses and private healthcare.

Benefits

Bonuses

Private healthcare

Pension scheme

Qualifications

• A degree with a 2:1 minimum in a relevant STEM subject.
• Strong background in data analysis, statistical modeling, and machine learning.
• Experience working with large datasets and utilizing data analysis tools.

Responsibilities

• Analyze and model large-scale PU datasets to extract performance insights.
• Develop and implement ML models to support reliability prediction.
• Collaborate with engineers to provide data-driven recommendations.

Skills

Data analysis

Statistical modelling

Machine learning

Data visualisation

Problem-solving

Effective communication

Education

Degree in relevant STEM subject

Tools

Python

MATLAB

Tableau

Power BI

Job description

Red Bull Powertrains has an exciting opportunity to join our technical team as we transition from building our team's first Power Units to refining them into a race‑ready package, all aimed at delivering the most competitive car on the 2026 Formula One grid. We are seeking a PU Data Scientist to join our Data Strategy & Insights department and drive the future of motorsport innovation. You will be based at our cutting‑edge engineering and manufacturing facility in Milton Keynes, designed to produce high‑performance Power Units for the 2026 engine regulations.

You will be a key part of the Team which is responsible for developing and applying statistical and machine learning techniques to analyse PU data, while supporting performance, reliability, and operational decisions.

You will be working closely with engineering and operational teams to uncover insights from live and historical PU data; building models and automation that influence both race‑day execution and long‑term performance.

Key responsibilities for this PU Data Scientist role are

• Analyse and model large‑scale PU datasets to extract performance insights and detect anomalies.
• Develop and implement ML models to support reliability prediction and fault detection.
• Collaborate with trackside engineers to provide data‑driven recommendations from PU adjustments and tuning.
• Lead exploratory data analysis on long‑term trends across tests and race events.
• Develop tools and automation for repeatable analytics workflows.
• Proactively explore and trial new statistical or computational approaches for PU optimisation.

Qualifications

• A degree with a 2:1 minimum in relevant STEM subject or related field.
• Strong background in data analysis, statistical modelling, and machine learning.
• Experience working with large datasets and utilising data analysis tools such as Python, MATLAB, or similar, with a focus on data science libraries.
• Familiarity with power unit systems, engine performance metrics, and Formula 1 racing rules and regulations.
• Proficient in data visualisation techniques and tools such as Tableau, Power BI, or similar.
• Excellent problem‑solving skills and ability to derive actionable insights from complex data.
• Strong attention to detail and ability to work under pressure in a fast‑paced racing environment.
• Effective communication and presentation skills, with the ability to convey technical information to both technical and non‑technical stakeholders.
• Passion for motorsports, Formula One racing, and a deep interest in power unit technology.

At Red Bull Powertrains, we push the limits, fight for every victory, and get things done with relentless focus. Joining us means being part of a high‑performance team that thrives on collaboration, trust, and ambition. We celebrate wins, learn from challenges, and take our work seriously—but not ourselves.

Benefits

• Bonuses
• Private healthcare
• A pension scheme