Product Data Scientist
Develops and deploys data-driven product insights at TripAdvisor, leveraging statistical modeling and experimentation to guide product decisions.
Skills required
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.
About the Role
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.
As a Product Data Scientist, you’ll be the analytical backbone of one or more product pods. You’ll own measurement and reporting, support experimentation, and surface the insights that drive product decisions — while actively developing your skills in more advanced analytics methods. This is a role for someone with strong foundational data science skills who is energised by the opportunity to grow.
What You’ll DoExperimentation
- Design and analyse A/B tests across Viator’s marketplace, applying sound statistical methods to interpret results and support confident decision-making
- Champion experimentation best practices: power calculations, guardrail metrics, and multiple testing corrections.
- Develop your mastery of causal inference and advanced experimentation techniques (e.g., difference-in-differences, propensity scoring, synthetic controls) as you apply them to answer questions that can't be randomized — such as measuring the impact of pricing changes on supplier retention or the long-term effect of personalization on traveler LTV.
Strategic Analysis & Measurement
- Own the measurement framework for your product area: define key metrics, build the instrumentation to track them, and surface insights that move the needle.
- Conduct exploratory analyses and deep dives into our data, using various data science approaches, to inform product decisions; for example, using tree-based or regression modeling to identify signals of high LTV
- Enable self-service through scalable datasets, metrics, dashboards and reporting frameworks
- Translate analytical outputs into actionable insights and clear product recommendations: not just ‘here’s the data,’ but ‘here’s what it means for the next sprint and how we should test it.’
Stakeholder Management & Communication
- Act as a thought partner with product managers and engineers to ensure the right data questions are being asked
- Translate analyses into clear narratives that are accessible to non-technical audiences — emphasising actionable insights and ‘so what’ over technical detail.
- Be a champion of unbiased, rigorous analysis — including when the data doesn’t support a stakeholder’s hypothesis; willingness to be the voice of inconvenient truths is a core expectation of this role.
- Several years in a data science, analytics, or quantitative research role at a data-driven organization; strong product analysts who are actively upskilling in data science methods are encouraged to apply
- Advanced SQL skills and hands-on experience querying and manipulating large datasets
- Proficiency with data visualisation tools (Tableau, Looker or equivalent)
- Experience with the full A/B testing process, from test design to results interpretation
- Some proficiency in Python for analysis, experimentation and exploratory modeling
- A track record of using data insights to influence product or business decisions
- Comfort with ambiguity: you can define a question when it isn’t handed to you, and you’re energised by incomplete information rather than paralysed by it
- A growth mindset: you’re actively upskilling in more advanced analytics methods and always willing to learn new tools and techniques
- Exposure to more advanced statistical methods and causal inference techniques — e.g. propensity scoring, synthetic controls, difference-in-differences, Bayesian approaches
- Familiarity with LLMs or NLP tooling for analytics use cases (e.g., content classification, dataset enrichment)
- Experience in travel or e-commerce; understanding of two-sided marketplace dynamics, geo-based demand variation, or supplier/consumer trade-offs
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