Tripadvisor - Product Data Scientist
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Requirements
• 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
Responsibilities
• Experimentation • Experimentation • 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.
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