Senior Data Scientist

Job description

OUR MISSION To become the car-changing destination of choice. By combining technology, media and deep automotive expertise, we've turned how people buy, sell, advertise and lease cars on its head. What started as a simple reviews site is now one of the largest online car-changing destinations in Europe. Last year alone we grew over 50% with nearly £3bn worth of cars bought on site, while £1.8bn of cars were listed for sale through our Sell My Car service. In 2024 we went big and acquired Autovia - creators of AutoExpress and Evo magazines - doubling our audience overnight. Together we now have one of the biggest YouTube https://www.youtube.com/carwow channels in the world with almost 10m subscribers and over 1.1 billion annual views, while we sell 1.2 million print copies of our magazines and have an annual web content reach over 350million. And we’re a long way from done! THE ROLE We're looking for a Senior Data Scientist to join our award-winning Data Science team at a pivotal moment. Carwow operates a two-sided marketplace — connecting car buyers and sellers at scale — and data science sits at the heart of how we make that marketplace smarter, faster, and more valuable for everyone in it. In fact, we recently won GenAI initiative of the Year at the British Data Awards. This is a hands-on, high-ownership role working centrally across the business. You'll partner with teams spanning Commercial, Marketing, Product, Finance, Engineering and Operations — developing and deploying ML and AI solutions that drive outcomes across both sides of our marketplace. The problems you'll work on are genuinely varied: pricing models, propensity and demand signals that sharpen marketing spend, personalised recommendations for our web product and CRM, and LLM-powered solutions for operational challenges like document verification. You'll translate ambiguous business problems into structured, production-ready solutions — and you'll be expected to deliver them end-to-end, from first principles through to being deployment-ready and beyond. WHAT YOU'LL BE DOING - End-to-End ML & AI Delivery: Lead data science initiatives from problem framing through to deployment, monitoring, and iteration — delivering the full production lifecycle. With no dedicated ML engineering function, you'll be responsible for ensuring your solutions are robust, scalable, and performing in the real world long after they ship. - GenAI & LLM Application: Design and build LLM-powered solutions where they create genuine business value — document processing, intelligent search, content understanding, and beyond. Apply them alongside classical ML with clear judgement about where each approach earns its place. - Commercial Impact: Connect your work directly to business outcomes. Whether you're building a model to improve marketing efficiency, a pricing signal to sharpen commercial decisions, or a recommendation engine to increase conversion — you understand the business lever you're pulling and design your solutions accordingly. - Prototyping & Experimentation: Move fast to test ideas before committing to full-scale development. Define rigorous success metrics upfront, validate honestly, and know when to double down and when to walk away. - Cross-Functional Partnership: Work closely with Commercial, Marketing, Product, Finance, Engineering and Operations stakeholders to understand problems deeply before reaching for a solution. Translate findings into clear, actionable narratives for both technical and non-technical audiences. - Standards & Craft: Contribute to shared best practices, documentation, and ways of working that raise the bar for the data science function — and help more junior team members grow alongside you. Drive continued adoption of AI capabilities to drive efficiencies, automation and constantly leverage new capabilities. WHAT YOU'LL NEED Please note: We know that no candidate will be the perfect match for all we've listed in this posting, so we’d encourage you to apply if you feel you're close to the brief but not an exact match. Ideally you’ll have - Commercial Mindset: You think about business impact first. You understand how your models connect to revenue, efficiency, or customer outcomes — and you use that to prioritise, scope, and communicate your work. - Stakeholder Partnership: Proven ability to work with commercial, marketing, and product stakeholders — translating business problems into well-scoped solutions and communicating technical solutions, challenges and outcomes clearly at all levels. - Sound Judgement: You navigate the tooling landscape with clear eyes — knowing when classical ML is right, when GenAI unlocks something new, and when a simpler solution is the more honest answer. Strong instincts for scalability, reliability, and explainability. - [Bonus] Marketplace or Two-Sided Platform Experience: Understanding of supply/demand dynamics and how data science creates leverage in a marketplace context. TEHCNICAL SKILLSET - Proven ML Experience: A strong track record of building, deploying, and maintaining ML models in Python in a production environment — not just notebooks. You've owned models after they ship and know how to keep them healthy. - Full-Lifecycle Delivery (MLOps): Comfortable delivering the end-to-end production lifecycle — model training, versioning, monitoring, and champion/challenger experimentation — without relying on a dedicated ML engineering team to carry that responsibility. - GenAI & LLM Expertise: Hands-on experience building LLM-powered solutions that deliver measurable business value. You understand how to apply, evaluate, and extend these tools — and you're honest about where they fall short. - Technical Depth: Solid experience in a cloud ML environment with software engineering principles — version control, code reviews, unit testing, and familiarity with containerisation. - Quantitative Rigour: Strong foundation in statistical evaluation and experiment design. You can define and defend success metrics, and you know when a model is degrading and what to do about it. - [Bonus] Experience with VertexAI TOOLS & TECHNOLOGIES - Languages: Python, SQL - Data & Transformation: dbt, Snowflake, BigQuery - Visualisation & BI: Looker - Engineering & MLOps: Docker, GitHub - Workflow & Orchestration: Vertex AI Pipelines (GCP), Kubeflow - LLMs & GenAI: Gemini API, Claude API INTERVIEW PROCESS - Step 1: People Team Screening Call (30 min) - Step 2: Hiring Manager Call: Experience (45 min) - Step 3: Technical Task: covering both Modelling & Production with Presentation (60 min + Task) - Step 4: Values Interview (45 min) WHAT’S IN IT FOR YOU - Hybrid working - Competitive salary to fund that dream holiday to Bali - Matched pension contributions for a peaceful retirement - Share options - when we thrive, so do you! - Vitality Private Healthcare, for peace of mind, plus eyecare vouchers - Life Assurance for (even more) peace of mind - Monthly coaching sessions with Spill - our mental wellbeing partner - Enhanced holiday package, plus Bank Holidays - 28 days annual leave - 1 day for your wedding - 1 day off when you move house - because moving is hard enough without work! - For your third year anniversary, get 30 days of annual leave per year - For your tenth year anniversary, get 35 days of annual leave per year - Option to buy 3 extra days of holiday per year - Work from abroad for a month - Inclusive parental, partner and shared parental leave, fertility treatment and pregnancy loss policies - Bubble childcare support and discounted nanny fees for little ones - The latest tech (Macbook or Surface) to power your gif-sending talents - Up to £500/€550 home office allowance for that massage chair you’ve been talking about - Generous learning and development budget to help you master your craft - Regular social events: tech lunches, coffee with the exec sessions, lunch 8 learns, book clubs, social events/anything else you pester us for - Refer a friend, get paid. Repeat for infinite money Diversity and inclusion is an integral part of our culture. We know that diverse teams are strong teams, so we welcome those with alternative identities, backgrounds, and experiences to apply for this position. We make recruiting decisions based on experience, skills and potential, so all our applicants are treated fairly and equally.