Location
London

Hours
Full Time

Salary
Negotiable

About the Role
At Tripadvisor, the Tripadvisor Group connects people to experiences worth sharing and aims to be the world’s most trusted source for travel and experiences. Leveraging our brands, technology, and capabilities, we connect a global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. Our subsidiaries include a portfolio of travel brands such as Tripadvisor, Viator, and TheFork.

As a Data Scientist on our experimentation team, you will collaborate with product teams across Viator to measure the impact of their changes, define meaningful metrics, and ensure sound decision-making. You will help make experimentation easier by developing reusable tooling, clear documentation, and consistent practices. Viator operates in a marketplace where experiences are often booked once a year and supply is finite and shared, requiring nuanced analysis to avoid misleading conclusions. You will receive support and mentorship from senior practitioners experienced in these challenges.

You will design and analyze experiments end to end, working closely with Product Managers and Engineers. You will translate product questions into testable hypotheses with pre-registered primary metrics and appropriate guardrails, ensuring an honest assessment of what the available traffic can detect. You will define and instrument feature-level metrics, investigate results thoroughly by checking assignment integrity and data quality, and treat surprising results as opportunities for diagnosis rather than announcements.

Additionally, you will build reusable queries, tooling, and templates to accelerate experimentation, communicate findings clearly to both technical and non-technical audiences, and look beyond whether a change worked to understand why it worked. You will also carry out analyses beyond experiments, including opportunity sizing, funnel and behavioral analysis, and observational measurement when randomization is not possible. Documentation of hypotheses, designs, outcomes, and decisions is essential to ensure comparability and organizational learning.

Continuous growth in experimentation and causal inference will be supported through review and mentorship from senior and principal data scientists.

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

Statistical & Experimentation Foundations
Sound understanding of experimentation beyond platform execution, including statistical power, minimum detectable effect, the difference between inconclusive results and no effect, issues caused by peeking and post-hoc metric selection, and common causes of invalid experiments.

Technical & Modelling Expertise
Strong proficiency in Python and SQL, hands-on experience with 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 operationalize product and feature-level metrics, with support on complex cases.

Partnership
Experience working closely with Product Managers and Engineers as a trusted partner, with a willingness to improve processes through tooling, documentation, and consistent practice.

Critical Thinking
Habit of questioning whether a result is trustworthy before interpreting its meaning, and confidence to communicate this constructively.

Communication
Clear written and verbal communication skills, able to explain statistical reasoning to non-statistical audiences and maintain constructive dialogue when results are unwelcome.

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

Preferred Qualifications
- 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 their importance for sensitivity.
- Exposure to causal inference methods for non-randomized situations.
- Experience with SaaS experimentation tools such as Statsig, Eppo, or GrowthBook, or with in-house platforms.
- Experience in high-scale consumer product environments such as marketplaces, e-commerce, or travel platforms.
- Interest in how modern AI tooling can accelerate analysis and make experimentation more accessible to product teams.