About this role
About Tripadvisor
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.
At Tripadvisor experiences, the only thing we love more than travel is data. We slice it, we dice it, and we use it to empower our decision making.
What You will do:
As a Principal Data Scientist you will be a leading individual contributor responsible for how experimentation works across the organisation.
You will lead through technical depth, setting the standards others work to and raising the quality of measurement and decision-making across the function.
You will build the capability that lets teams experiment well without central support: the standards, tooling and protocols that make good practice the default, and a strategy for how it develops over time.
Both the speed and the reliability of decision-making should improve as a result. You will also take on the measurement questions we cannot currently answer well, where traffic is thin or the outcomes that matter take months to appear.
You will:
• Set the technical standard for experimentation across Product Data Science, from conventional A/B testing to quasi-experimental and Bayesian methods, and make it practical through protocols, frameworks and tooling.
• Partner with Product, Engineering and Data Platform teams to improve experimentation velocity without sacrificing rigour.
• Critically assess how experimentation and the decisions that follow it affect platform health and growth, and make that relationship visible to leadership.
• Own the measurement approach for Viator's most complex questions, where standard experimentation is insufficient and the method has to be designed rather than selected.
• Develop and validate the statistical methods the organisation relies on, including simulation-based verification that they behave correctly before teams depend on them.
• Act as the final technical authority on measurement validity, adjudicating disputed results and determining what the evidence does and does not support.
• Standardise recurring analytical and experimentation processes across the function, using automation and AI capabilities where they materially improve consistency, throughput or quality.
• Design and land methodological improvements with organisation-wide impact, such as variance reduction for difficult metrics, approaches for low-traffic surfaces, proxy and surrogate metrics for long-horizon outcomes, and designs that hold up where users compete for shared supply.
• Grow the technical depth of the wider team