$173,000 – $242,500
Listed on Expedia Group’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads machine learning systems that power personalized CRM campaigns at Expedia, determining which customers to target, when to reach them, and what offers to make. It suits experienced ML scientists who want to own production systems end-to-end, work across business and engineering teams, and influence retention and growth strategies for a global travel company.
Our summary, not Expedia Group’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- Master's or PhD in quantitative field (Operations Research, Applied Mathematics, Statistics, Economics, Computer Science) or equivalent professional experience
- 6+ years (Master's) or 4+ years (PhD) applying machine learning to real-world problems with production ML systems that demonstrated business impact
- Proficiency in supervised, unsupervised, and statistical modeling with depth in at least one relevant area
- Strong experimentation and statistics fundamentals including A/B testing and experiment design
- Python, SQL, and distributed data processing (Spark/Databricks)
- Solid software engineering practices and familiarity with modern AI development tools
- Experience leading cross-functional ML projects with clear communication to technical and non-technical audiences
Nice to have
- Deep knowledge of constrained optimization, operations research, or budget allocation methods
- Deep understanding of causal inference with experience measuring incremental effects in observational settings
- Experience with CRM personalization, loyalty marketing, incentive optimization, or customer retention systems
- Experience with deep learning, reinforcement learning, or multi-armed bandits in production decision systems
- Experience with customer lifetime value modeling, churn prediction, or propensity scoring
- Hands-on ML production practices including CI/CD for ML, model monitoring, observability, and automated pipelines