Data Scientist

Role overview Develop statistical models and analyses that inform pricing, bidding, and conversion decisions across flight and hotel products. The work centers on causal inference: determining whether a business decision drove an outcome, while accounting for shifts in users, markets, and measurement. You’ll own the reasoning and validation behind models and partner with engineers to deploy and monitor them.

Responsibilities - Design experiments and quasi-experimental analyses to estimate the effects of pricing, bidding, and conversion decisions. - Model price response and tradeoffs, translating results into decisions with clear objectives and constraints. - Build probability models that are calibrated and useful when data is sparse or conditions change. - State and assess identification assumptions; quantify uncertainty with scenarios and sensitivity checks. - Evaluate models against meaningful baselines and check calibration, bias, and stability across segments. - Work with engineers and stakeholders to deploy, monitor, and assess models, documenting assumptions and limitations.

Requirements - Three to eight years in data science, applied statistics, or a related field, including practical causal inference. - Strong grounding in experimental and quasi-experimental design, probability calibration, and statistical evaluation. - Experience using models to inform decisions and evaluate their impact, including production deployment and monitoring with engineers. - Fluency in SQL and Python, plus practical experience with Bayesian modeling, confounding, selection effects, or limited data. - Ability to select interpretable methods, explain assumptions, and distinguish predictive accuracy from evidence of causal impact. - Sound independent judgment when using AI coding or analysis tools; ability to make work reproducible and communicate uncertainty clearly.

Nice to have Experience in risk or actuarial modeling, insurance analytics, travel, pricing, marketplaces, ranking, or personalization.