Product Data Scientist

The Tripadvisor Group connects people to experiences worth sharing, aiming to be the world’s most trusted source for travel and experiences. Its brands include Tripadvisor, Viator, and TheFork, focused on travel content, guidance, and marketplaces.

• Several years in data science, analytics, or quantitative research in a data-driven organization.
• Advanced SQL skills and experience querying and manipulating large datasets.
• Proficiency with data visualization tools such as Tableau or Looker.
• Experience with the full A/B testing process from design to interpretation.
• Some proficiency in Python for analysis, experimentation, and exploratory modeling.
• Track record of using data insights to influence product or business decisions.
• Ability to define questions independently and work with ambiguous or incomplete information.
• Growth mindset with active upskilling in advanced analytics methods.

Nice to have:

• Exposure to advanced statistical methods and causal inference techniques like propensity scoring and Bayesian approaches.
• Familiarity with LLMs or NLP tooling for analytics use cases such as content classification.
• Experience in travel or e-commerce with understanding of two-sided marketplaces and geo-based demand variation.

Experimentation:

• Design and analyse A/B tests across Viator’s marketplace using sound statistical methods.
• Champion experimentation best practices including power calculations and multiple testing corrections.
• Develop mastery of causal inference and advanced experimentation techniques such as difference-in-differences and propensity scoring.

Strategic Analysis & Measurement:

• Own the measurement framework for assigned product area, defining key metrics and building instrumentation.
• Conduct exploratory analyses using various data science approaches including tree-based and regression modeling.
• Enable self-service through scalable datasets, metrics, dashboards, and reporting frameworks.
• Translate analytical outputs into actionable insights and clear product recommendations.

Stakeholder Management & Communication:

• Act as a thought partner to product managers and engineers for data questions.
• Translate analyses into clear narratives accessible to non-technical audiences.
• Champion unbiased, rigorous analysis including when data challenges stakeholder hypotheses.

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