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. It leverages its brands, technology, and capabilities to connect a global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. Subsidiaries include Tripadvisor, Viator, and TheFork.

• Several years in data science, analytics, or quantitative research at a data-driven organization; strong product analysts upskilling in data science are encouraged.
• Advanced SQL skills with 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.
• Proven track record of using data insights to influence product or business decisions.
• Comfort with ambiguity and ability to define questions with incomplete information.
• Growth mindset with active upskilling in advanced analytics methods and willingness to learn new tools and techniques.

Nice to have:

• Exposure to 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 such as content classification and dataset enrichment.
• Experience in travel or e-commerce, understanding two-sided marketplace dynamics, geo-based demand variation, or supplier/consumer trade-offs.

Experimentation:

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

Strategic Analysis & Measurement:

• Own the measurement framework for your product area by defining key metrics and building instrumentation.
• Conduct exploratory analyses and deep dives using data science approaches like tree-based or 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 with product managers and engineers to ensure relevant data questions are asked.
• Translate analyses into clear narratives accessible to non-technical audiences.
• Champion unbiased, rigorous analysis, including presenting inconvenient truths when data does not support hypotheses.