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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 in Europe, with nearly £3bn in cars bought on-site annually and a major media presence including a 10m-subscriber YouTube channel and print magazines. Carwow recently won GenAI Initiative of the Year at the British Data Awards.
You will join the award-winning Data Science team as a Senior Data Scientist in a hands-on, high-ownership role working centrally across the business. You'll partner with Commercial, Marketing, Product, Finance, Engineering, and Operations teams to develop and deploy ML and AI solutions that drive outcomes across both sides of the marketplace. Problems span pricing models, propensity and demand signals for marketing optimization, personalized recommendations for web products and CRM, and LLM-powered solutions for operational challenges like document verification.
Key responsibilities include:
- Leading data science initiatives end-to-end from problem framing through deployment, monitoring, and iteration, with full ownership of the production lifecycle
- Designing and building LLM-powered solutions for document processing, intelligent search, content understanding, and similar applications
- Connecting work directly to business outcomes—whether improving marketing efficiency, sharpening commercial pricing 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
- Working closely with cross-functional stakeholders to understand problems deeply and translate findings into clear, actionable narratives for 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
You will be expected to deliver solutions end-to-end without a dedicated ML engineering function, ensuring robustness, scalability, and real-world performance.
REQUIREMENTS:
- Commercial Mindset: Think about business impact first; understand how models connect to revenue, efficiency, or customer outcomes; use this to prioritize, scope, and communicate work
- Stakeholder Partnership: Proven ability to work with commercial, marketing, and product stakeholders; translate business problems into well-scoped solutions; communicate technical solutions, challenges, and outcomes clearly at all levels
- Sound Judgement: Navigate the tooling landscape with clear eyes; know when classical ML is appropriate, when GenAI unlocks something new, and when a simpler solution is more honest; strong instincts for scalability, reliability, and explainability
- Proven ML Experience: Strong track record of building, deploying, and maintaining ML models in Python in production environments (not just notebooks); experience owning models after deployment and keeping them healthy
- Full-Lifecycle Delivery (MLOps): Comfortable delivering end-to-end production lifecycle—model training, versioning, monitoring, and champion/challenger experimentation—without relying on a dedicated ML engineering team
- GenAI & LLM Expertise: Hands-on experience building LLM-powered solutions that deliver measurable business value; understand how to apply, evaluate, and extend these tools; honest assessment of limitations
- Technical Depth: Solid experience in cloud ML environments with software engineering principles—version control, code reviews, unit testing, and familiarity with containerization
- Quantitative Rigour: Strong foundation in statistical evaluation and experiment design; ability to define and defend success metrics; know when a model is degrading and what to do about it
- Technical Stack: Proficiency in Python and SQL; experience with dbt, Snowflake, BigQuery, Looker, Docker, GitHub, Vertex AI Pipelines (GCP), Kubeflow, Gemini API, and Claude API
- Bonus: Marketplace or two-sided platform experience; VertexAI experience
The interview process includes a 30-minute People Team screening call, 45-minute hiring manager experience call, 60-minute technical task covering modeling and production with presentation, and 45-minute values interview.
