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The Tripadvisor Group connects people to experiences worth sharing, and aims to be the world's most trusted source for travel and experiences. We leverage our brands, technology, and capabilities to connect our global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), include a portfolio of travel brands and businesses, including Tripadvisor, Viator, and TheFork.

As the Senior Director, Machine Learning, you will spearhead our Machine Learning, Data Science and Experimentation efforts, reporting to the Vice President of Engineering. You will play a pivotal role in shaping our ML and AI strategy and ensuring we leverage our data assets to their full potential to drive growth and competitive advantage. This spans the personalization, recommendation, and ranking systems that power how travelers discover and book experiences; rigorous experimentation; and the responsible application of generative AI - all built on our rich first-party data.

You will run your organization as a hub-and-spoke: a strong central team that owns talent, craft standards, career growth, tooling, and long-horizon ML strategy, with scientists deployed close to the product areas they serve so delivery stays fast and grounded in real business context. You will a strategic leader and a credible technologist: a player-coach who sets technical direction, holds the scientific bar, and stays close enough to the work to earn the trust of a demanding team. You will lead and grow the organization through a layer of managers as it scales.

• Build and lead a unified ML organization. Consolidate and grow ML scientists currently embedded across engineering into one hub-and-spoke model; define the operating model, decision rights, and service commitments so product areas keep momentum through the change.
• Own the personalization strategy. Set the technical direction for the recommendation, ranking, and search-relevance systems that personalize the traveler journey - multi-objective ranking, sequential and session-based recommenders, embeddings and semantic retrieval, and real-time features - the models that drive discovery and conversion at scale.
• Lead data science and experimentation. Own predictive modeling, causal inference, and the experimentation platform and standards - A/B testing, guardrail metrics, and rigorous measurement of incremental business impact - as a decision-science partner to Product and the business, championing a strong experimentation culture across the organization.
• Shape our generative-AI direction. Own the strategy for applying LLMs and generative AI across the Experiences product - conversational trip planning, retrieval-augmented generation grounded in our first-party reviews and content, and automated content - with rigorous evaluation to control quality and cost, including agentic and LLM-driven experiences as this work scales.
• Grow, develop, and retain the team . Shape ML and data-science career ladders and dual technical/management tracks; partner with Talent Acquisition to attract and hire top-tier ML and data-science talent; calibrate hiring and promotions; establish standards for the research-to-production model lifecycle; and develop both the management track and the senior IC bench - Staff and Principal Scientists.
• Own production ML health. Set and enforce the standards for reliable, well-monitored, cost-effective models in production - deployment, retraining, monitoring and governance - and the service levels (SLAs) of the ML organization. Partnering with MLOps infrastructure team.
• Partner across the company. Translate commercial strategy into an ML roadmap with Product, Engineering, Design, and Analytics leadership; represent ML in executive planning; and connect model performance to business outcomes.

Substantial experience in machine learning, together with significant experience leading managers and senior technical teams in a complex, scaled organisation.

A track record of shipping production machine learning at scale - ideally personalization, recommendations, search/ranking, or similar - to large consumer traffic, with measurable business impact.

Experience building, scaling, or restructuring an ML or data-science o