Tripadvisor - AI Analytics Specialist
Requirements
• A strong analyst first — SQL, data modeling, and structured thinking are your foundation, not just a line on your CV • Genuinely excited about AI: you have been building with it, have explored agentic workflows and conversational analytics tools, and have clear views on where it creates real value versus noise • You see a broken workflow and think about how to fix it — you are drawn to real pain points and you know the difference between solving something and patching it • As comfortable running a data literacy workshop for a marketing team as discussing semantic layer design with a data engineer — you move fluently between both worlds • Structured, self-directed, and delivery-oriented — you bring rigor to an enablement program the same way an engineer brings it to code • Bachelor's degree in Analytics, Statistics, Data Science, Computer Science, or a related quantitative field required; Master's degree preferred. Equivalent practical experience considered. • 3-5 years in analytics or data science, ideally in travel, ecommerce, or a marketplace environment • Strong SQL and hands-on experience with modern BI and analytics tooling (Looker, Tableau, Hex, dbt, or similar) • Proven ability to make analytical complexity accessible and actionable for both technical and non-technical stakeholders • Track record of driving adoption of new tools or analytical ways of working across teams • Active, hands-on experience with AI tools — prompt engineering, LLM-powered workflows, or conversational analytics — applied to real problems • Exposure to agentic analytics concepts or tools, and a view on where they create practical value • Familiarity with semantic layer concepts and tooling (dbt metrics, Cube, LookML, or equivalent) • Understanding of marketing analytics: attribution, funnel analysis, segmentation, and campaign measurement • Python for lightweight automation or tooling • Background in data literacy programs, analytics enablement, or internal CoE initiatives • What Makes This Role Different • 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? • AI Analytics Specialist • Makes data questions answerable by anyone • Owns the capability that powers many domains • Redesigns them around AI and self-service • Builds data literacy so colleagues find their own answers • Designs agentic workflows that run end to end • Brings technical and non-technical teams through the change together • Fluent across the AI analytics landscape — semantic layer, conversational tools, automation • Proactively identifies where AI creates the most leverage • Success = decision made faster, without waiting for an analyst • Traditional analytics • Answers data questions • Owns a dashboard or domain • Works with existing workflows • Communicates findings • Automates individual reports • Translates data for stakeholders • Depth in one tool or stack • Reactive to requests • Success = insight delivered
Responsibilities
• 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. • Diagnose and fix real friction. • 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. • Make data conversational. • 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. • Shape the semantic layer. • 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. • Define agentic workflow opportunities. • 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. • Automate what should not need a human. • 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. • Build org-wide data literacy.
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