Senior Data Scientist

Overview

As a Senior Data Scientist at Carwow, you lead end-to-end ML and AI initiatives in a fast-paced two-sided marketplace. You’ll collaborate with Commercial, Marketing, Product, Finance, Engineering and Operations to deploy models that impact pricing, marketing efficiency, recommendations and operations. You’ll apply GenAI and classic ML judiciously, delivering production-ready solutions and driving real business value. This role offers ownership, growth, and the opportunity to shape a data-driven product at scale.

Pay / Benefits

• Hybrid working
• Competitive salary
• Matched pension
• Share options
• Vitality Private Healthcare
• Learning and development budget

Responsibilities

• Own the full ML/AI production lifecycle from problem framing to deployment, monitoring and iteration
• Design and deploy GenAI/LLM-powered solutions alongside traditional ML
• Directly drive commercial outcomes through models that improve marketing efficiency, pricing signals or recommendations
• Prototype rapidly, define success metrics, and validate ideas before full-scale development
• Collaborate with Commercial, Marketing, Product, Finance, Engineering and Operations to translate problems into actionable solutions
• Contribute to best practices, documentation, and ways of working to raise data science standards and mentor juniors, while expanding AI capabilities

Key requirements

• Proven ML experience in Python in production, with model ownership post-deployment
• Full lifecycle ownership (MLOps) without a dedicated ML engineering team
• GenAI & LLM expertise with measurable business impact
• Commercial mindset focusing on revenue, efficiency or customer outcomes
• Sound judgement on when to apply classical ML vs GenAI and on scalability, reliability and explainability
• Technical depth in cloud ML environments (e.g. Vertex AI, SageMaker) and strong software engineering fundamentals (version control, testing, containers)
• Stakeholder partnership with commercial, marketing, and product teams, translating problems into scoped solutions
• Quantitative rigour in evaluation and experiment design; ability to detect model degradation and respond

• Cross-functional collaboration
• Clear communication to technical and non-technical audiences
• Strategic thinking with business-minded prioritisation
• Python
• SQL
• TensorFlow

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