About Wego

Wego is an online travel metasearch platform used by millions to find the best flights and hotel deals. We are seeking a talented Data Scientist with a strong statistical background to join our Data Analytics & Data Science team.

About the role

As part of the Data Analytics & Data Science team, you will build the statistical models that power pricing, bidding, and conversion optimization across Wego's metasearch marketplace and our owned-inventory hotel and flight products. This means building economic models of user and market behavior, then translating them into the product experience through ranking, personalization, and other user-facing surfaces.

You will work daily alongside data scientists, analysts, and engineers, using causal inference and Bayesian methods to turn business questions into models that are both statistically sound and production-ready. You will partner closely with engineering to ship your models as reliable services, and with the wider analytics team to raise the statistical bar across the org.

Key Responsibilities

• Causal Inference & Experimental Design — Design causal inference and quasi-experimental approaches for pricing and conversion measurement across our products.

• Probability & Conversion Calibration — Build and calibrate probability and conversion models that feed optimization routines across our marketplace, distribution, and pricing products.

• Elasticity & Gain/Loss Modeling — Build elasticity and gain/loss models to power bidding and pricing optimization decisions.

• Model Validation & Method Selection — Select and validate statistical methods that best fit the data and business problem at hand.

• Ranking & Personalization — Build ranking and relevance models for sort order and personalization across our flight and hotel search products, backed by calibrated conversion and relevance estimates.

• Cross-Functional Collaboration — Partner with engineering to productionize models, and with analytics and product teams to translate business questions into measurable, testable hypotheses.

Qualifications

Experience & Domain Expertise

• 3-8 years of experience in data science, applied statistics, or a related field, with hands-on experience in causal inference and Bayesian statistics.

• Strong background in experimental and quasi-experimental design, probability calibration, and statistical model evaluation.

• Proven track record of delivering statistical work that directly informed business decisions.

• Experience building production ML systems.

• Travel-domain experience is a plus.

Technical Skills

• Fluency in SQL and Python.

• Hands-on experience with causal inference methods, Bayesian modeling, and experimental / quasi-experimental design in production settings.

• Experience building ranking or relevance models (e.g. learning-to-rank, personalization, search/recommendation ranking).

• Experience working with cloud platforms (e.g. Google Cloud Platform, AWS, or Azure) for data storage, model development, and deployment.

• Comfortable working alongside AI tools (eg. Claude Code, Codex) to accelerate model development and analysis.

• Strong analytical rigor and sound judgment in choosing the right statistical approach for the problem, rather than defaulting to the most complex option available.

Soft Skills & Mindset

• High agency and comfort working through ambiguity, with AI as a core part of how you work — you actively use AI tools day to day and continuously refine your own workflows with them to raise your productivity.

• Team player who works well cross-functionally and contributes to the team beyond their own workstream.

• Willingness to learn and explore new domains — comfortable taking on projects outside prior experience and ramping up quickly.

• Clear communicator, able to explain statistical tradeoffs to both technical and non-technical stakeholders.