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Carwow is a two-sided automotive marketplace connecting car buyers and sellers at scale across Europe. The company operates one of the largest online car-changing destinations, processing nearly £3bn in car transactions annually, and recently acquired Autovia (AutoExpress and Evo magazines), bringing a combined audience of 10m YouTube subscribers and 350m+ annual web reach.
You'll join the award-winning Data Science team as a Senior Data Scientist in a high-ownership, hands-on role working centrally across the business. This is a pivotal moment for the data science function—the team recently won GenAI Initiative of the Year at the British Data Awards.
Your responsibilities will span end-to-end ML and AI delivery: framing ambiguous business problems, building production-ready solutions, and owning them through deployment and beyond. You'll work on genuinely varied problems—pricing models, propensity and demand signals for marketing optimization, personalized recommendations for web and CRM, and LLM-powered solutions for operational challenges like document verification.
You'll partner closely with Commercial, Marketing, Product, Finance, Engineering, and Operations teams. Key aspects of the role include:
- Leading data science initiatives from problem framing through deployment, monitoring, and iteration, delivering the full production lifecycle without a dedicated ML engineering function
- Designing and building LLM-powered solutions (document processing, intelligent search, content understanding) alongside classical ML, with clear judgment about where each approach adds value
- Connecting your work directly to business outcomes—whether improving marketing efficiency, sharpening commercial decisions, or increasing conversion through recommendation engines
- Moving fast to prototype and test ideas before full-scale development, defining rigorous success metrics and validating honestly
- Translating technical findings into clear, actionable narratives for both technical and non-technical audiences
- Contributing to shared best practices, documentation, and ways of working that raise the bar for the data science function and help junior team members grow
The role requires a commercial mindset, strong stakeholder partnership skills, and sound judgment in navigating the ML/GenAI tooling landscape. Bonus experience includes marketplace or two-sided platform background.
Technical Requirements:
- Proven track record building, deploying, and maintaining ML models in Python in production environments (not just notebooks), with ownership of models post-deployment
- Full-lifecycle delivery capability (MLOps): model training, versioning, monitoring, champion/challenger experimentation without relying on dedicated ML engineering support
- Hands-on experience building LLM-powered solutions that deliver measurable business value; understanding of how to apply, evaluate, and extend these tools
- Cloud ML environment experience with software engineering principles: version control, code reviews, unit testing, containerization familiarity
- Strong foundation in statistical evaluation and experiment design; ability to define and defend success metrics and detect model degradation
- Bonus: VertexAI experience
Required Tools & Technologies:
- Languages: Python, SQL
- Data & Transformation: dbt, Snowflake, BigQuery
- Visualization & BI: Looker
- Engineering & MLOps: Docker, GitHub
- Workflow & Orchestration: Vertex AI Pipelines (GCP), Kubeflow
- LLMs & GenAI: Gemini API, Claude API
