Data Scientist, Experimentation in London

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 Data Scientist on our experimentation team you will work with product teams across Viator to measure whether the changes they ship actually worked, defining the metrics that matter and making sure the decisions that follow are sound. Part of your contribution is making experimentation easier for the teams you support, through reusable tooling, clear documentation and consistent practice. Viator sells experiences that travellers often book once a year, in a marketplace where supply is finite and shared, so you will be learning to navigate problems where the obvious analysis can give the wrong answer, with support from senior practitioners who have done it before.

You will:

• Design and analyse experiments end to end with the product teams you support, working directly with the Product Managers and Engineers who will act on the results.
• Turn product questions into testable hypotheses with pre-registered primary metrics, appropriate guardrails, and an honest view of what the available traffic can and cannot detect before the experiment starts.
• Define and instrument feature-level metrics, understanding how metric choice affects sensitivity, interpretation and the decision a team is trying to make.
• Investigate results properly, checking assignment integrity, exposure and data quality before conclusions are drawn, and treating a surprising result as something to diagnose rather than announce.
• Build reusable queries, tooling and templates and apply our shared protocols, so teams can run good experiments faster and with less rework.
• Communicate findings clearly to technical and non-technical audiences, including inconclusive and negative results, with a recommendation attached rather than a table of numbers.
• Look beyond whether a change worked to why it worked, and flag when a result may not hold for other users, markets or time periods.
• Carry out analysis beyond experiments, including opportunity sizing, funnel and behavioural analysis, and observational measurement where randomisation isn't possible.
• Document hypotheses, designs, outcomes and decisions so results remain comparable and the organisation compounds what it learns.
• Grow your own depth in experimentation and causal inference, with review and mentorship from senior and principal data scientists.

Skills & Experience

Experience:

• Solid experience in data science or a similar quantitative role, supporting and influencing product teams.

Statistical & Experimentation Foundations:

• Sound understanding of experimentation beyond running it through a platform, including statistical power and minimum detectable effect, the difference between an inconclusive result and no effect, why peeking and post-hoc metric selection cause problems, and the common ways an experiment can be invalid.

Technical & Modelling Expertise:

• Strong proficiency in Python and SQL, with hands-on experience of statistical analysis and experimentation, and some exposure to statistical modelling or machine learning techniques such as regression and classification.

Product Acumen:

• Ability to define, implement and operationalise product and feature-level metrics, with support from senior colleagues on the most complex cases.

Partnership:

• Experience working closely with Product Managers and Engineers as a trusted partner, and a willingness to make things easier for those teams through tooling, documentation and consistent practice.

Critical Thinking:

• A habit of asking whether a result is trustworthy before asking what it means, and comfort saying so when it isn't.

Communication:

• Clear written and verbal communication, with the ability to explain statistical reasoning to people without a statistical background and to hold your position constructively when a result is unwelcome.

Education:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.

You could be an especially great fit if you have:

• Experience with metrics that are difficult to measure, such as sparse conversion, heavy-tailed revenue, or slow-to-observe outcomes.
• Familiarity with variance reduction techniques and why they matter for sensitivity.
• Exposure to causal inference methods for situations where randomisation isn't available.
• Experience with SaaS experimentation tools such as Statsig, Eppo or GrowthBook, or with an in-house platform.
• Experience in a high-scale consumer product environment such as a marketplace, e-commerce or travel platform.
• An interest in how modern AI tooling can make analysis faster and experimentation more accessible to product teams.

We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at.