Senior Data Scientist

Overview

In this Senior Data Scientist role at Carwow, you’ll own the end-to-end data science lifecycle to drive value across buyers and sellers in a two-sided marketplace. You’ll partner across Commercial, Marketing, Product, Finance, Engineering and Operations to deploy ML/AI solutions that impact pricing, demand signals, and personalized experiences. Expect hands-on work with GenAI/LLMs and traditional ML to solve real business problems at scale. You will shape the data function, promote AI adoption, and deliver deployment-ready solutions.

Pay / Benefits

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

Responsibilities

• Lead end-to-end ML & AI projects from framing to deployment and monitoring
• Design and implement GenAI/LLM-powered solutions (e.g., document processing, intelligent search)
• Drive commercial impact through models that improve marketing efficiency, pricing, and recommendations
• Prototype rapidly with rigorous success metrics and decide when to scale or pivot
• Collaborate with Commercial, Marketing, Product, Finance, Engineering, and Operations stakeholders
• Contribute to standards, documentation, and best practices for the data science function and AI adoption

Key requirements

• Commercial mindset and ability to tie models to revenue and outcomes
• Strong stakeholder partnership with cross-functional teams
• Sound judgement on when to use classical ML vs GenAI and focus on scalability, reliability, and explainability
• Bonus: marketplace or two-sided platform experience
• Proven ML experience in Python in production and ownership of models post-shipment
• Full-lifecycle delivery capabilities (MLOps) without relying on a dedicated ML engineering team
• GenAI & LLM expertise with hands-on implementation experience
• Cloud ML environment experience with software engineering practices (versioning, testing, containerisation)
• Quantitative rigour with strong evaluation and experimentation skills

• Business-minded communication
• Cross-functional collaboration
• Strong problem framing and storytelling
• Python and SQL
• ML model development, deployment, and monitoring
• MLOps and end-to-end production lifecycle

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