Our Technology Team partners with teams across Expedia Group to create innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences that drive loyalty and traveler satisfaction.

We’re hiring a hands-on high-performing individual contributor to own and evolve a core Vacation Rental KPI. You will be responsible for turning complex data into clear, actionable insights for senior leaders.

You will lead analysis projects end-to-end—designing and building datasets, creating robust analytical products, and delivering high-impact dashboards and recommendations. You’ll follow team-wide visualization standards and use AI/LLM tools pragmatically to create efficiency and scale.

In this role, you will:

• Own a key KPI and its dashboard: Define, build, and maintain a stakeholder-facing dashboard that becomes the single source of truth for KPI health and performance.

• Lead end-to-end analytics projects (scoping, data engineering, analysis, visualization, delivery) that drive measurable improvements in supply, quality, and conversion.
• Build reliable, repeatable datasets and pipelines from multiple sources using SQL and scripting (Python/R), with strong attention to performance and data quality.
• Create clear, persuasive visualizations and interactive dashboards (e.g., Tableau, Plotly, ggplot) for both technical and non‑technical audiences, with a strong focus on storytelling and decision support.
• High visibility: Own a core VR KPI and dashboard used by senior leaders to steer the VR business.
• Direct, measurable impact: Your insights and recommendations will shape how we grow and optimize Vacation Rental supply.
• Ownership and leadership: Lead analytics workstreams end-to-end, from problem framing to final recommendations and follow-through.
• Modern toolkit: Work in a collaborative environment that embraces modern analytics, engineering, and AI tools to increase impact and scale.
• Automate recurring reports and monitoring, including data-quality checks and alerting, to ensure stakeholders always have trusted, up-to-date views on key metrics.
• Introduce and operationalize AI/LLM tools (e.g., for code generation, documentation, insight summarization, report automation) where they meaningfully increase productivity and scale.
• Collaborate with product, marketing, operations, and data science teams to frame questions, translate business problems into analytical workstreams, and present trade-offs and recommendations.
• Produce reproducible code and documentation, sharing artifacts via Git, Confluence, and team knowledge bases.
• Apply statistical methods and intermediate ML approaches (e.g., regression, classification, clustering) when appropriate; partner with central Data Science to validate and scale models.
• Design and analyze measurement strategies and experiments (A/B tests, pre/post analyses, causal impact, multivariate tests) for initiatives affecting VR inventory.

Experience & Qualifications:

• Degree in Mathematics, Statistics, Computer Science, Economics, Engineering, or related field (Master’s is a plus), or equivalent practical experience.
• 2–3 years in a comparable analytics or data science role (with relevant degree), or 4+ years of directly relevant industry experience.
• Advanced SQL (joins, window functions, performance tuning).
• Strong proficiency in Python or R for data wrangling, analysis, and automation.
• Experience combining multiple data sources and building repeatable, well-documented datasets.
• Visualization & storytelling: Solid experience designing and maintaining dashboards in Tableau or similar BI tools.
• Ability to translate complex analyses and metrics into simple, intuitive visuals and narratives for non-technical stakeholders.
• Statistics & experimentation:Working knowledge of applied statistics and basic ML (e.g., AB testing, statistical significance, regression, pre/post and causal impact analyses).
• Data engineering mindset: Familiarity with data modeling, pipeline design, and best practices for performance, reliability, and data quality.
• Communication & stakeholder management:Excellent written and verbal communication skills; able to listen, gather requirements, and proactively manage expectations.
• Comfortable presenting findings and recommendations to senior and executive stakeholders.
• Ways of working: Critical thinker with strong business acumen and a bias for action.
• Collaborative mindset and willingness to coach junior colleagues and share best practices.
• Familiarity with AI/LLM tools and enthusiasm for using them to automate workflows, speed analyses, and scale insight delivery.