Product Data Scientist

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.