Principal Data Scientist, Experimentation

Tripadvisor · London

  • Senior
  • Full-time
  • Posted 2026-09-24
  • Confirmed live on 25 September 2026

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Job description

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 by reviewing designs, mentoring senior data scientists, and improving how people reason about measurement.

• Advise on where experimentation is the wrong tool and define what should be done instead.

Skills & Experience:

• Experience: Extensive experience in data science or a similar quantitative role, with a proven track record of supporting and influencing a product organisation.

• Statistical & Experimentation Expertise: Authoritative command of experimentation in all its forms, from experimental design and variance reduction to causal inference, bandits and Bayesian methods. You should be able to develop and validate methodology, not only apply it.

• Technical & Modelling Expertise: Expert level proficiency in Python and SQL. Deep, hands-on experience with statistical modelling, (quasi) experimentation, multi-arm bandits, and a wide range of machine learning techniques such as regression, classification and clustering.

• Product Acumen: Demonstrated ability to define, implement and operationalise crucial product and feature-level metrics from scratch.

• Partnership & Enablement: Demonstrated ability to improve how other teams work by providing guidance, tooling and protocols, increasing both the speed and the quality of their experimentation rather than absorbing the work yourself.

• Decision Impact & Platform Health: Ability to critically assess how product decisions affect platform health and growth over time, and to bring that perspective into how experiments are designed and interpreted.

• Standardising at Scale: Experience standardising processes, frameworks or methods across multiple teams, including the use of AI and automation to make good practice the default.

• Cross-Functional Partnership: Proven ability to build strong relationships and drive outcomes across Product, Engineering, Data Platform and other central functions, often without direct authority.

• Critical Thinking: Leader in critical thinking, with a demonstrated habit of establis

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