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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 Europe's largest online car-changing destinations, processing nearly £3bn in car transactions annually, and recently acquired Autovia (AutoExpress and Evo magazines), expanding its reach to 10m YouTube subscribers and 350m+ annual web content impressions.
You'll join the award-winning Data Science team as a Senior Data Scientist in a high-ownership, hands-on role working across the entire business. This is a pivotal moment for the organization, and data science sits at the heart of making the marketplace smarter and more valuable for both buyers and sellers.
Key responsibilities include:
- End-to-End ML & AI Delivery: Own data science initiatives from problem framing through deployment, monitoring, and iteration. You'll deliver the full production lifecycle without a dedicated ML engineering function, ensuring solutions are robust, scalable, and performing in production.
- GenAI & LLM Application: Design and build LLM-powered solutions for genuine business value—document processing, intelligent search, content understanding—and apply them alongside classical ML with clear judgment about where each approach fits.
- Commercial Impact: Connect your work directly to business outcomes. Whether building pricing models, demand signals for marketing efficiency, personalized recommendations, or LLM-powered operational solutions, you understand the business lever you're pulling.
- Prototyping & Experimentation: Move fast to test ideas before full-scale development. Define rigorous success metrics upfront, validate honestly, and know when to double down or walk away.
- Cross-Functional Partnership: Work closely with Commercial, Marketing, Product, Finance, Engineering, and Operations stakeholders. Translate findings into clear, actionable narratives for technical and non-technical audiences.
- Standards & Craft: Contribute to shared best practices and documentation that raise the bar for the data science function. Help junior team members grow and drive adoption of AI capabilities for efficiency and automation.
Required experience:
- Proven ML track record: Building, deploying, and maintaining ML models in Python in production environments (not just notebooks). You've owned models post-deployment and know how to keep them healthy.
- Full-Lifecycle Delivery (MLOps): Comfortable with end-to-end production lifecycle—model training, versioning, monitoring, champion/challenger experimentation—without relying on dedicated ML engineering support.
- GenAI & LLM Expertise: Hands-on experience building LLM-powered solutions with measurable business value. You understand how to apply, evaluate, and extend these tools and are honest about limitations.
- Technical Depth: Cloud ML environment experience with software engineering principles—version control, code reviews, unit testing, containerization familiarity.
- Commercial Mindset: You think about business impact first, understand how models connect to revenue or customer outcomes, and use that to prioritize and communicate work.
- Stakeholder Partnership: Proven ability to work with commercial, marketing, and product stakeholders, translating business problems into well-scoped solutions.
- Sound Judgment: Navigate the tooling landscape with clear eyes, knowing when classical ML is right, when GenAI unlocks something new, and when simpler solutions are more honest.
Bonus: Marketplace or two-sided platform experience, understanding supply/demand dynamics and how data science creates leverage in marketplace contexts.
