Lead Data Scientist

About the role

Brego is an automotive technology company using AI and data analytics to help dealerships, lenders, and other industry partners make better vehicle valuation, pricing, and risk decisions. Working at the intersection of software, data, and decision-making, the team focuses on turning complex information into practical products that support smarter outcomes across the automotive market. As a Lead Data Scientist, you will take ownership of the AI (custom neural networks rather than third-party LLM technology) and machine learning capabilities behind products that influence high-value pricing and risk decisions. This is a hands-on technical leadership role where you will be responsible for designing, building, deploying, and continuously improving production machine learning systems from end to end. You will own the full lifecycle of models, from feature engineering and training through deployment, monitoring, retraining, and ongoing optimisation, working independently while collaborating closely with engineering and product teams to deliver measurable business impact. Responsibilities Own the end-to-end lifecycle of production machine learning models, from problem definition through deployment and ongoing optimisation. Design, build and deploy artificial neural network and machine learning models for vehicle valuation, pricing and other analytics. Take responsibility for production model performance, reliability and long-term maintenance. Evaluate model performance and improve predictive accuracy across production models. Develop and maintain automated retraining pipelines to keep models effective over time. Monitor deployed models, investigate issues and implement improvements to ensure models remain accurate and reliable. Design and run experiments, track results and use data to drive model improvements. Work closely with engineering and product teams to integrate models into production systems and deliver business value. Must have: 5+ years of experience building and deploying machine learning models in production environments. Strong experience developing and training neural networks for real-world applications. Strong experience with the Python data science ecosystem, including pandas, NumPy and scikit-learn. Hands-on experience with PyTorch or TensorFlow. Strong understanding of machine learning, statistics, and model evaluation methodologies. Experience taking machine learning models from concept through deployment and ongoing production ownership. Experience evaluating model performance, improving predictive accuracy, and maintaining retraining pipelines, model monitoring, and experiment tracking. Experience with feature engineering and working with large, real-world datasets. Experience writing clean, maintainable, production-quality Python code. Experience with SQL for data analysis and data manipulation. Experience deploying ML workloads in cloud environments. Ability to independently own technical projects and make sound engineering decisions with minimal supervision. Strong problem-solving skills with the ability to investigate complex data and modelling challenges. Strong communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders. Experience collaborating with software engineers, product managers, and data engineers. Eligible to work in the UK. Nice to have: Experience in the automotive industry or with vehicle data. Experience in pricing, forecasting, risk modelling, or other predictive analytics domains. Experience with MLOps tooling and infrastructure. Experience building automated data and model pipelines. Experience mentoring or providing technical leadership to other data scientists or engineers. Competitive salary of £90,000 - £110,000 per year, depending on experience. Private healthcare. Pension scheme. Fully remote role with flexible working hours. Working from home allowance. Choice of Apple MacBook Pro or high-spec Windows workstation. Learning and progression opportunities. Optional access to our Silverstone office. The team usually meets there around one day per week, but attendance is entirely optional. High levels of ownership and autonomy with the opportunity to shape the company's AI strategy. Collaborative, low-bureaucracy engineering culture that values autonomy, integrity and innovation. Regular company social events. 25 days annual leave plus 3 additional days between Christmas and New Year.

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