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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** Carwow - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Unit Testing, BigQuery, Code Review, Github, Python (Programming Language), Machine Learning, Tensorflow, Standard Sql, Management of Software Versions, Large Language Models, Snowflake, Generative AI, Build Management, Machine Learning Operations, Marketplace, Looker Analytics, Software Version Control, Docker - **Published:** July 11, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=68c648d4259c780b ## About the Role * Proven ML Experience: A strong track record of building, deploying, and maintaining ML models in Python in a production environment - not just notebooks. You've owned models after they ship and know how to keep them healthy. * Full-Lifecycle Ownership (MLOps): Comfortable owning the end-to-end production lifecycle - model training, versioning, monitoring, and champion/challenger experimentation - without relying on a dedicated ML engineering team to carry that responsibility. * GenAI & LLM Expertise: Hands-on experience building LLM-powered solutions that deliver measurable business value. You understand how to apply, evaluate, and extend these tools - and you're honest about where they fall short. * Commercial Mindset: You think about business impact first. You understand how your models connect to revenue, efficiency, or customer outcomes - and you use that to prioritise, scope, and communicate your work. * Sound Judgement: You navigate the tooling landscape with clear eyes - knowing when classical ML is right, when GenAI unlocks something new, and when a simpler solution is the more honest answer. Strong instincts for scalability, reliability, and explainability. * Technical Depth: Solid experience in a cloud ML environment (e.g. Vertex AI, SageMaker) with strong software engineering fundamentals - version control, code reviews, unit testing, and familiarity with containerisation. * Stakeholder Partnership: Proven ability to work with commercial, marketing, and product stakeholders - translating business problems into well-scoped solutions and communicating outcomes clearly at all levels. * Quantitative Rigour: Strong foundation in statistical evaluation and experiment design. You can define and defend success metrics, and you know when a model is degrading and what to do about it. * Desirable - Marketplace or Two-Sided Platform Experience: Understanding of supply/demand dynamics and how data science creates leverage in a marketplace context. TOOLS & TECHNOLOGIES * Languages: Python, SQL * ML & AI Frameworks: TensorFlow, Vertex AI * LLMs & GenAI: Gemini API, Claude API * Data & Transformation: dbt, Snowflake, BigQuery * Visualisation & BI: Looker * Engineering & MLOps: Docker, GitHub * Workflow & Orchestration: Vertex AI Pipelines INTERVIEW PROCESS * Step 1: People Team Screening Call (30 min) * Step 2: Hiring Manager Call: Experience (45 min) * Step 3: Technical Task: covering both Modelling & Production with Presentation (60 min + Task) ## Description * End-to-End ML & AI Ownership: Lead data science initiatives from problem framing through to deployment, monitoring, and iteration - owning the full production lifecycle. With no dedicated ML engineering function, you'll be responsible for ensuring your solutions are robust, scalable, and performing in the real world long after they ship. * GenAI & LLM Application: Design and build LLM-powered solutions where they create genuine business value - document processing, intelligent search, content understanding, and beyond. Apply them alongside classical ML with clear judgement about where each approach earns its place. * Commercial Impact: Connect your work directly to business outcomes. Whether you're building a model to improve marketing efficiency, a pricing signal to sharpen commercial decisions, or a recommendation engine to increase conversion - you understand the business lever you're pulling and design your solutions accordingly. * Prototyping & Experimentation: Move fast to test ideas before committing to full-scale development. Define rigorous success metrics upfront, validate honestly, and know when to double down and when to walk away. * Cross-Functional Partnership: Work closely with Commercial, Marketing, Product, Finance, Engineering and Operations stakeholders to understand problems deeply before reaching for a solution. Translate findings into clear, actionable narratives for both technical and non-technical audiences. * Standards & Craft: Contribute to shared best practices, documentation, and ways of working that raise the bar for the data science function - and help more junior team members grow alongside you. 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