Job Description

We’re on a mission to help people discover the real value of travel — to inspire, to give more reasons, to make it easy — for you to go. Our company was founded back in 2005, and since then, we’ve imagined and created some of the most well-loved products for travelers all around the world.

Today, Wego is used by millions of people every month — people who travel for adventure, work, family, and for many other reasons. That’s why we work tirelessly to make your experience of planning & booking flights, hotels, and trips as seamless as possible.

Team Description:

You'll join Wego's Data Analytics & Data Science team, which works across our Flights and Hotels products, from the metasearch marketplace and ad units to our OTA business, in markets worldwide.

Our roadmap has more high-impact projects than ever, and we're growing the team to deliver the core Flights and Hotels work that needs strong data science and statistical skills.

The Role:

You will build statistical models that guide pricing, bidding and conversion decisions across Wego's metasearch marketplace and our hotel and flight products. Causal inference is central to the role: you'll separate the effect of a decision from changes in users, markets and measurement.

We value modeling experience from risk, insurance, actuarial or other fields where calibrated probabilities and uncertainty affect real decisions. You'll own the statistical reasoning, validation and interpretation, and partner with engineering to put models into production.

What you will be working on:

• Designing experiments and quasi-experimental analyses that show the real effect of pricing, bidding and conversion decisions
• Estimating price response and gain/loss tradeoffs, and turning them into pricing and bidding decisions with clear objectives and constraints
• Building calibrated probability models that hold up under sparse data and changing conditions
• Finding opportunities in product behavior and business performance, and carrying them through to an evaluated outcome

Core Responsibilities:

• Make identification assumptions explicit, test their plausibility and explain what the evidence supports
• Quantify uncertainty using scenario analysis and sensitivity checks
• Compare methods against useful baselines and check calibration, bias and stability across segments
• Distinguish predictive performance from evidence about the effect of a decision
• Partner with engineers to ship models, define monitoring and investigate performance changes
• Document assumptions and limitations so others can use models correctly
• Validate the value of new opportunities with stakeholders and define success upfront
• Help analysts and product teams frame testable questions, and explain uncertainty and tradeoffs in practical terms

Skills and experience:

• 3–8 years of experience in data science, applied statistics or a related field, with practical experience in causal inference
• Strong grounding in experimental and quasi-experimental design, probability calibration and statistical model evaluation
• A record of using models to inform decisions and evaluate their impact
• Experience developing models for production and working with engineers on deployment and monitoring
• Fluency in SQL and Python
• Practical experience with causal inference and Bayesian modeling, including confounding, selection effects and limited data
• Ability to choose interpretable statistical methods that fit the problem and explain their assumptions
• Experience with cloud platforms for data access, model development and deployment
• Fluency with AI coding and analysis tools, such as Claude Code or Codex, combined with independent statistical judgment
• You spot worthwhile problems without waiting to be assigned, validate why they matter and own the solution through to measurable impact
• You align priorities with stakeholders, then work independently through ambiguity and missing context
• You use AI fluently but own the output: you check generated analysis against the data and challenge a convincing result when the evidence doesn't support it
• You make analyses reproducible and own defects in the models and conclusions you deliver
• You communicate with precision, share knowledge and learn new domains quickly

Additional Info:

• Experience with risk or actuarial modeling, insurance analytics or comparable work involving uncertainty and decision consequences is a strong plus
• Travel, pricing, marketplace, ranking or personalization experience is a plus