Lead Data Scientist

About Brego: Brego is an innovative automotive technology company using AI and advanced data analytics to help dealerships, lenders, and key industry partners make smarter vehicle valuation, pricing, and risk decisions. Working at the frontier of machine learning, software, and financial technology, Brego turns complex vehicle records into high-performance products that drive measurable commercial impact across the automotive market. Our team operates with an elite, low-bureaucracy engineering culture where individual autonomy and high standards are celebrated.

Position Overview

We are seeking a highly autonomous, systems-fluent, and machine-learning proficient Lead Data Scientist to take technical ownership over the proprietary neural network frameworks and predictive intelligence powering Brego’s platforms under a permanent, full-time fully remote layout in the United Kingdom (with optional access to our Silverstone hub). In this hands-on technical leadership seat, you will be the single accountable owner designing, building, deploying, and maintaining production-grade machine learning models from end to end. Shifting completely away from third-party wrappers, routine administrative entry tasks, or entry-level web template modifications, you will run an active custom deep neural network, high-concurrency automated retraining pipeline, and cloud-native MLOps architecture laboratory. Partnering closely beside engineering and product teams, you will optimize asset valuation engines that directly influence high-value trading and credit risk decisions. This position requires a machine learning authority with 5+ years of production experience who handles extensive datasets fluidly natively using Machine Learning, TensorFlow, and Python Scripting primitives, demonstrates rock-solid statistical judgment, and shapes the company’s core AI roadmap independently.

Key Responsibilities

• Core AI Strategy & Architecture Governance: Own the long-term lifecycle of production machine learning systems, designing and building custom artificial neural networks for real-world valuation and pricing systems natively utilizing Machine Learning best practices.
• End-to-End Model Lifecycle Control: Take technical responsibility for production model performance, reliability, and automated feature engineering—navigating architectures from problem definition through training and cloud deployment.
• Automated Pipeline Infrastructure: Design, deploy, and maintain robust automated retraining loops and telemetry metrics to keep predictive assets effective over time.
• Vulnerability & Accuracy Diagnostics: Investigate complex data modeling anomalies, monitor live system parameters, and continuously optimize model accuracy to safeguard platform predictive integrity.
• Data Engineering & Sourcing Operations: Clean, structure, and manipulate large, messy real-world datasets across production repositories using advanced SQL query pipelines.
• Cross-Functional System Synchronization: Partner close-knit next to software engineers, data architects, and product managers to seamlessly integrate deep learning workloads into production cloud environments.
• Technical Thought Leadership: Champion robust, secure-by-design coding conventions across the data space, writing clean, maintainable, and production-ready code natively using the Python Scripting ecosystem.
• Strategic Technical Communication: Translate complex algorithmic behavior, statistical limitations, and experimental outcomes into scannable insights for both engineering squads and non-technical commercial stakeholders.

Required Skills & Qualifications

• A minimum of 5+ years of proven, successful professional technology experience building, training, and deploying machine learning models independently within live production environments.
• Expert Deep Learning Command: Meticulous hands-on experience designing and optimizing custom neural network architectures natively utilizing TensorFlow or PyTorch frameworks (experience with native network builds rather than shallow API wrappers is mandatory).
• Complete, production-grade proficiency navigating the modern data science toolset natively leveraging Python Scripting (specifically pandas, NumPy, and scikit-learn) and SQL .
• Demonstrated background managing full-lifecycle modeling tracks—encompassing custom feature engineering, automated validation matrices, model tracking, and regression diagnostics.
• Outstanding problem-solving skills, with a proven ability to independently own high-impact technical initiatives and make sound architectural decisions with minimal supervision.
• Location & Legal Context: Position operates under highly flexible remote guidelines open exclusively to qualified systems data scientists who reside permanently inside and hold legal authorization to work within the United Kingdom .

Preferred Strategic Indicators (Nice to Have)

• Prior platform or quantitative research background operating directly inside the automotive technology sector, asset valuation spaces, algorithmic pricing engines, or credit risk modeling .
• Direct operational familiarity with advanced MLOps automation infrastructures, automated ETL pipelines, or cloud data warehouses.
• Proven experience mentoring, coaching, or providing technical direction to other data scientists and infrastructure engineers.

What We Offer

• Top-Tier UK Artificial Intelligence Remuneration Matrix: A highly competitive annual base salary scale of £90,000 - £110,000 per year calibrated precisely to your deep learning pedigree, supplemented by a dedicated pension scheme.
• 100% remote work infrastructure autonomy open across the UK with highly flexible working hours and a tailored working-from-home allowance.
• Venture-Scale Engineering Footprint: Elite career credentials built by hand-picking and architecting the primary neural networks driving asset pricing decisions for major lenders and dealerships.
• Comprehensive private healthcare preservation setups designed to shield you and your dependents seamlessly.
• Access to premium technical provisions with your choice of an Apple MacBook Pro or a high-spec Windows workstation, 25 days of annual leave plus 3 additional collective days off between Christmas and New Year, continuous learning opportunities, and optional access to our elite Silverstone workspace for collaborative team syncs once per week.