Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success.
Why Join Us?
To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win.
We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.
Data Scientist I – TEaL Analytics
Join TEaL Analytics at Expedia Group and use the power of data to shape how millions of travelers engage with our loyalty programs, incentives, and CRM marketing. As a Data Scientist I, Analytics you’ll partner with teams across the business to turn complex datasets into clear insights, experiments, and models that drive member growth, engagement, and smarter decisions.
What You’ll Do
- Apply analytics principles and team playbooks to solve well‑defined business problems with close guidance and support from your manager.
- Extract, transform, and analyze data from multiple sources to build datasets for modeling, reporting, and deep‑dive analysis.
- Design and execute simple experiments (e.g., A/B tests, pre/post, causal impact studies) to evaluate loyalty strategies, incentives, and CRM campaigns.
- Apply and interpret descriptive statistics and basic probability concepts (e.g., statistical significance vs. exploratory analysis, logistic regression outputs) to answer business questions.
- Build and interpret foundational models such as linear and logistic regression, understanding their assumptions and when they are appropriate for loyalty and marketing use cases.
- Create clear, inclusive visualizations and dashboards that communicate insights to both technical and non‑technical audiences, following standard chart principles (e.g., labeling, titling, appropriate chart types).
- Collaborate with stakeholders to refine project goals, iterate on solutions, and deliver practical, data‑driven recommendations.
- Write efficient, reproducible code and documentation (e.g., in SQL, Python, R), and maintain clear annotations and comments to support peer review.
- Enact basic data quality checks for reports and analyses, seeking peer reviews and guidance as needed to ensure high data quality.
Contribute to a culture of peer review, knowledge sharing, and continuous improvement, actively seeking feedback and upskilling opportunities.
Experience and Qualifications
- 0–2 years of experience as a Bachelor’s/Master’s graduate, and/or relevant industry experience, and/or completed data apprenticeships/certifications.
- Bachelor’s or Master’s degree in Mathematics, Statistics, Computer Science, Data Science, Economics, or a related quantitative field (or equivalent practical experience).
- Proven use of data to deliver insights (for example, dashboards, reports, or analytics projects used by stakeholders).
Over time, showing signs of growing confidence and the ability to pick up larger or less well‑defined tasks with less support.
Technical Skills
- Beginner–intermediate proficiency in SQL plus at least one of Python, R, or similar for data extraction, transformation, analysis, and visualization.
- Solid grasp of descriptive statistics and basic probability, including A/B testing fundamentals and significance vs. exploratory reads.
- Introductory experience with data modeling (e.g., linear/logistic regression) and experiment design (A/B, pre/post, simple causal impact).
Familiarity with data visualization tools and principles (e.g., Tableau, CJA), and awareness of working with large, complex datasets.
Core Competencies
- Analytical thinking: Inquisitive, structured approach to problem solving; comfortable breaking business questions into measurable components.
- Data literacy & quality: Finds and evaluates relevant data sources, applies basic data quality checks, and documents work clearly for peer review.
- Communication & storytelling: Explains methods and findings succinctly to technical and non‑technical partners, focusing on insights and business impact.
- Collaboration & learning: Works transparently with stakeholders, seeks feedback and peer reviews, and proactively builds domain knowledge in travel and loyalty.
- Inclusive mindset: Designs visuals and narratives that are accessible and understandable to audiences with varying technical backgrounds.
Accommodation requests
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