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.

INTRODUCTION TO TEAM

The Traveler Business Team builds and drives growth for our global consumer businesses—Expedia, Hotels.com, and Vrbo. This division creates compelling and differentiated traveler value for each brand by setting the strategic vision, operating strategy, and plan. Responsibilities include investment allocation and prioritization, P&L accountability, and leading cross-functional teams across Expedia Group, who are all held accountable to a single scorecard.
The Marketing Measurement Analytics team is an integral part of the Traveler Business Team. They are looking for a high performing individual contributor to hold responsibility for maintaining and improving Marketing Mixed Model (MMM) operations and sign-off, ultimately ensuring they are business-ready. If you are someone who consistently applies and enhances analytics capabilities, best practices and processes to solve complex business issues and identify opportunities then this could be the role for you!

EXPERIENCE AND QUALIFICATIONS:

• PhD, Masters, or Bachelors (preferably in Mathematics or Scientific degree).
• 7+ years of work experience OR 4+ years in a comparable data analytics role.
• Experience delivering data-driven insights and recommendations through multiple projects.
• Advanced experience using R, Python, or SQL for data analysis, structuring, transforming, and visualizing big data.
• Experience delivering analytics projects to different business areas with strong business acumen.
• Understanding of MMM & incrementality testing techniques.
• Critical thinking, problem-solving, communication, influencing, information gathering, listening, and statistics skills.
• Data visualization skills for communicating results to stakeholders of different technical levels.
• Basic machine learning concepts and approaches.

• Extract data from multiple sources and combine into required datasets for model building or analytics.
• Challenge MMM concepts and outputs based on specific modelling, marketing, and business knowledge.
• Collaborate with subject matter experts and stakeholders to clarify business questions and enhance feature selection.
• Apply probability, frequentist vs Bayesian statistics, and statistical concepts like regression, ANOVA, AB testing.
• Select appropriate measurement techniques/designs to answer business questions and explain trade-offs.
• Define and build data models, interpret outputs, and iterate to improve models.
• Provide guidance and coaching on statistical techniques.
• Understand common data models, their assumptions, and best data sources.
• Learn new modelling approaches and critically evaluate their benefits.
• Refine modelling project questions, drive model design decisions, and provide recommendations.
• Deliver iterative project steps, refine requirements, and evolve based on learnings.
• Create data pipelines and workflows, considering legal implications.
• Create shareable code and documentation.
• Develop clear visualizations to support data stories.
• Apply inclusive design principles to visualizations.
• Use common charting packages in scripting languages and seek alternatives when needed.
• Provide constructive feedback to upskill teammates.
• Build trust and collaborate transparently with stakeholders.
• Articulate project goals, methodology, caveats, and conclusions to technical and non-technical audiences.
• Present insights clearly and concisely, seeking feedback and actioning it.
• Create relevant artifacts like technical documentation, presentations, and executive summaries.
• Understand the organization’s processes, objectives, and challenges.
• Work with big data, explaining challenges and solutions to partners.
• Write advanced SQL and understand different SQL flavors and querying tools.
• Know important data sources and support channels to resolve data issues.
• Use best practices for data quality checks and query optimization.
• Write shareable, efficient code for data pipelines.
• Adopt and evaluate new querying tools and datasets.
• Frame complex business problems as analytics problems and break them into manageable tasks.
• Work with stakeholders and analytics peers to identify the right objective and propose solutions appropriate for the task and timeframe.
• Demonstrate iterative thinking and identify next steps based on findings.
• Pick analytically valid approaches, favoring iterative delivery that solves for the objective, not just the ask.
• Communicate regularly with stakeholders, addressing problems and meeting key deadlines.
• Proactively resolve problems, identify opportunities, and collaborate with team members.
• Automate repeated measurement and reporting tasks, build scalable dashboards, and train stakeholders.
• Identify and reach out to relevant domain experts and stakeholders to maximize impact.