Scientific Software Developer - F1 Motorsport

Data Science Talent
1 year ago
Applications closed

Related Jobs

View all jobs

Fully funded 4-year PhD Studentship in Chemistry - Machine Learning-Accelerated Quantum Chemica[...]

Data Scientist Biologicals Research

Data Scientist Biologicals Research

Data Engineer

Data Engineer

Senior Data Scientist

Scientific Software Developer - F1 Motorsport

Location: South East England


-----


2.62 seconds.That's how long it takes to service an entire F1 racing car at a pit stop.


2 weeks.That's how quickly your contributions can make a visible impact on the track.

Join one of the world's leading Formula 1 teams, where every role plays a pivotal part in enhancing car performance and speed. Here, the collective effort truly outpaces the sum of its parts.


-----


The Role


As a Scientific Software Developer within the Data Science team, you'll provide essential software engineering support focused on data applications and systems. Your work will involve:


  • Engaging with Data:Handle data from advanced aerodynamic testing environments, including wind tunnels, computational fluid dynamics, and on-track testing. Collaborate with the Vehicle Performance Group (VPG) to delve into data from race tracks and simulation settings.


  • Software Development:Design, develop, and optimise software applications and tools tailored to the needs of their data pipelines.


  • Algorithm Creation:Develop algorithms that transform raw data into actionable insights, directly influencing data-driven decisions.


  • Coding Excellence:Oversee and enforce coding standards and best practices within the data science group to ensure high-quality, maintainable code.


  • Collaborative Problem-Solving:Work closely with data scientists, engineers, and stakeholders to understand requirements and translate them into effective software solutions.


-----


Your Profile


  • Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field. Advanced degrees or coursework in a scientific discipline are advantageous.


  • Proven experience in software development within data-intensive environments.


  • Proficiency in Python and its scientific computing libraries (NumPy, SciPy, pandas, scikit-learn) is essential, as well as knowledge of Parallel and High Performance Computing.


  • Experience in creating algorithms to derive insights from data and developing robust data pipelines for efficient processing, storage, and retrieval.


  • Adept at quickly understanding and addressing software or computer science challenges, grasping underlying mechanics to create effective solutions.


-----


Apply Now


Be part of a world-class team where your contributions can drive tangible results seen by millions worldwide. To explore this opportunity further and learn about the rewards on offer, click the 'Easy Apply' button.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.