Data Scientist, Experimentation

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

• 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.

• 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 AccessibleRecruiting@tripadvisor.com.

If you have any additional questions about careers at Tripadvisor you can email us at recruitment@tripadvisor.com. We have all the answers!

#LI-Hybrid

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• 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.