Senior Machine Learning Scientist - CRM Marketing

Expedia•Seattle, WA

9h•Hybrid

About The Position

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. This role is part of a focused machine learning science team within Marketing that builds the ML systems behind personalized CRM offers for Expedia Group’s travelers. Our models determine which customers to reach, when to engage them, and what incentive to offer — powering retention, reactivation, and growth campaigns that touch hundreds of millions of travelers worldwide. We continuously improve the algorithms that power campaign targeting - moving toward fully ML-driven personalization at scale - and this role will help define that technical roadmap.

Requirements

A Master’s or PhD in Operations Research, Applied Mathematics, Statistics, Economics, Computer Science, or a related quantitative field; or equivalent related professional experience
6+ years (Master’s) or 4+ years (PhD) of experience applying machine learning to real-world problems, with a track record of delivering production ML systems that created measurable business impact
Proficiency across core ML methods (supervised, unsupervised, and statistical modeling) with demonstrated depth in at least one area relevant to this role
Strong experimentation and statistics fundamentals: designing rigorous experiments (A/B and beyond), selecting appropriate methods, and producing reliable, accurate analyses that inform high-stakes business decisions
Fluency in Python, SQL, and distributed data processing (Spark/Databricks), solid software engineering practices, and familiarity with modern AI development tools
Leader of cross-functional ML projects — aligning stakeholders on problem framing, success metrics, and delivery timelines — and can communicate findings clearly to both technical and non-technical audiences

Nice To Haves

Deep knowledge of constrained optimization, operations research, or budget allocation methods — designing systems that balance reach, relevance, and return on investment under real-world constraints
Deep understanding of causal inference — including the assumptions, limitations, and failure modes of observational methods — with experience applying these techniques to measure incremental effects in real-world settings
Experience with CRM personalization, loyalty marketing, incentive optimization, or customer retention systems
Experience with deep learning, reinforcement learning, or multi-armed bandits applied to real-world decision systems
Experience with customer lifetime value modeling, churn prediction, or propensity scoring
Hands-on ML production practices: CI/CD for ML, model monitoring, observability, and automated pipelines

Responsibilities

Help define the ML science roadmap: Identify the highest-impact ML opportunities for CRM personalization, sequence initiatives against business strategy, and translate a multi-year vision into concrete, deliverable projects with clear milestones and measurable outcomes.
Build and own production ML systems: Lead the full lifecycle — from problem framing and metric design through data exploration, modeling, evaluation, deployment, and iteration — for systems that run daily at scale, in partnership with engineering.
Partner across the business: Work with marketing to understand customer and campaign objectives, with analytics to shape measurement strategies, and with engineering to deliver reliable production systems — bringing business acumen and domain depth to every technical decision.
Evolve experimentation and measurement: Strengthen how we test hypotheses and quantify impact — finding smarter, faster ways to validate ideas, reduce uncertainty, and build confidence in ML-driven decisions before and after they reach production.
Tell the data story: Communicate findings, trade-offs, and recommendations clearly to technical and business audiences through effective data visualization and narratives that influence priorities and build stakeholder confidence.
Raise the bar: Mentor scientists through code reviews and design discussions, drive adoption of modern AI tools and best practices, and champion standards for scientific rigor, reproducibility, and documentation.