Tripadvisor에서 AI Analytics Specialist를 채용합니다. 데이터 분석 역량과 AI 기술을 결합하여 조직의 데이터 활용 방식을 혁신할 전문가를 찾습니다. SQL 및 데이터 모델링에 능숙하며, 에이전트 워크플로우와 대화형 분석 도구 구축 경험이 필수입니다. 마케팅 팀과 협업하여 데이터 접근성을 높이고 자동화된 인사이트 시스템을 설계하는 역할을 수행합니다.
New
London
We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique perspective and experiences, so we can collectively revolutionize travel and together find the good out there.
Tripadvisor is the world’s largest online travel site, visited by 390 million travellers each month, and our Experiences business, Viator, is a fast-evolving and highly data-driven part of the organisation.
At Viator, data is at the heart of how we build great products. We use it to understand our customers, improve decision-making, and drive measurable business impact.
The Analytics & ML Marketing team is looking for an AI Analytics Specialist who is, at their core, a data analyst — and who is genuinely obsessed with how AI can change the way companies work with data. Not someone who has read about it, but someone who has been building with it, has opinions on it, and is energized by the idea of applying it at scale inside a real organization.
This role sits at the crossroads of analytical craft, AI fluency, and organizational change. You will not be expected to build every piece of the puzzle alone — but you will be the person who understands how the pieces fit, defines what needs to exist, drives it forward with engineers and data teams, and makes sure the whole organization can actually use it. You will diagnose real pain points, shape the right solutions, and bring both technical and non-technical colleagues along for the transformation.
Diagnose and fix real friction. Talk to marketing colleagues across performance, CRM, personalization, and incentives. Understand where data access is slow, where workflows are overcomplicated, and where decisions get made without the right information — then define and drive solutions that fix those problems at the root, not the surface.
Make data conversational. Champion and help deploy AI-powered interfaces — natural language querying, automated insight summaries, smart alerting — so that any marketing colleague can interrogate data directly, without writing SQL or raising a ticket.
Shape the semantic layer. Work with data and engineering teams to ensure our metrics are canonically defined, consistently named, and structured in a way that makes self-service reliable and AI tools trustworthy. You will not build it alone — but you will own the vision and drive the standards.
Define agentic workflow opportunities. Identify where agentic analytics systems — pipelines that monitor performance, surface anomalies, and trigger proactive insight — would unlock the most value, and work with the right teams to bring them to life.
Automate what should not need a human. Spot recurring analytical workflows — weekly reports, campaign snapshots, performance reviews — and push to replace manual effort with automated, AI-assisted pipelines through prompt engineering and workflow design.
Build org-wide data literacy. Create the tools, training, and shared frameworks that genuinely shift how the marketing organization works with data — making AI tools and self-service analytics accessible to everyone, not just analysts.
Traditional analytics roles are built around depth — owning a domain, producing analysis, answering questions well. This role is built around breadth and leverage: how do you make the whole organization better at using data, not just the analytics team?
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