Data Scientist II - Marketing Automation Performance Analytics

Expedia•Seattle, WA

3d•Hybrid

About The Position

Expedia Group is seeking an innovative Data Scientist to transform how we understand and optimize organic search performance across SEO and AEO (AI-enabled search). You'll build and maintain the AI and automation layer that powers performance intelligence for Expedia Group's retail brands including Expedia, Hotels.com, and Vrbo, enabling faster, deeper, and more scalable analysis without linearly scaling manual effort. This role sits at the intersection of analytics, automation, and AI. You'll design agent-assisted workflows, automate recurring performance analysis, build proactive anomaly detection systems, and create feedback loops that continuously improve how the team diagnoses performance drivers, identifies opportunities, and supports strategic decisions. You'll partner closely with marketing analysts and leadership to translate business questions into automated analytical workflows, leveraging tools like Claude, GPT, SQL, Python, and internal data platforms. If you're passionate about using AI to make analytics teams more effective, not just faster at doing the same work, this is your opportunity to build the operating model for marketing performance in the age of AI.

Requirements

2+ years of experience in a highly analytical role (marketing analytics, data science, business intelligence, marketing automation, or comparable product analytics role)
Bachelor's or Master's with analytical focus (Computer Science, Data Science, Mathematics, Economics, Statistics, etc.) or equivalent related professional experience
Proven ability to translate ambiguous business problems into structured, repeatable analytical solutions
Experience with marketing analytics platforms and datasets (Google Analytics, Adobe Analytics, Tableau, or similar BI tools); organic search (SEO/AEO) knowledge is a strong plus
Results-driven with an operational mindset, focused on building tools and systems that deliver sustained value, not just one-off analyses
Excellent communication and collaboration skills; able to work cross-functionally with analysts, product managers, and business stakeholders to co-design solutions
Self-directed learner who thrives in a fast-paced environment and is comfortable iterating on solutions based on feedback and changing requirements
Strong Skills in SQL and Python; comfortable building automated pipelines and integrating multiple data sources

Nice To Haves

Demonstrated proficiency in AI and automation applications within analytics or marketing, specifically experience building workflows with GenAI to automate analysis, reporting, or insight generation.
Hands-on experience with prompt engineering, retrieval-augmented generation (RAG), embeddings, or multi-modal AI applications
Familiarity with organic search (SEO) or agentic search (AEO) performance drivers, ranking systems, and key metrics
Experience building or contributing to analytics platforms, data pipelines, or internal tools that scale analytical capabilities
Background in growth marketing, performance marketing, or e-commerce analytics
Knowledge of advanced statistical or machine learning techniques applied to marketing measurement or optimization problems

Responsibilities

Design, build, and maintain an automation and AI layer for recurring performance workflows including weekly/monthly reporting, anomaly detection, and performance deep dives
Develop agent-assisted analytical workflows that accelerate root-cause diagnosis, surface insights earlier
Partner with performance analysts to translate business questions into reusable, automated frameworks that improve over time through feedback loops
Create clear, actionable outputs (automated commentary, visualizations, structured insights) that reduce manual effort while maintaining analytical rigor and business context
Support performance deep dives with rapid data exploration, segmentation analysis, and diagnostic workflows that combine domain knowledge with technical automation
Continuously refine prompts, agent logic, and analytical outputs based on stakeholder feedback, changing KPIs, algorithm updates, and evolving business questions