Director, Machine Learning Science - Marketing

Expedia Expedia · Hospitality · Seattle, WA

Director of Machine Learning Science for Marketing at Expedia Group, leading an applied science team to build and optimize ML models for online advertising bidding and capital allocation. The role involves owning the strategy, roadmap, and OKRs, delivering production-grade ML systems, applying rigorous experimentation, recruiting and developing talent, and partnering with cross-functional leadership.

What you'd actually do

  1. Own the strategy, roadmap, and OKRs for the machine learning systems powering Marketing
  2. Design and deliver production-grade ML models and optimization systems that improve bidding, capital allocation, and ROAS
  3. Apply rigorous experimentation and measurement to validate business impact
  4. Recruit, develop, and retain applied machine learning scientists and managers, and support their growth in a complex environment
  5. Prioritize the team's investment across platform migration, new capabilities, and model innovation

Skills

Required

  • Graduate degree in machine learning, computer science, statistics, or a related quantitative field; or equivalent related professional experience
  • 10+ years of relevant professional experience
  • 5+ years of people management experience, including leading high-performing machine learning teams
  • Track record of delivering high-impact machine learning products from concept to production at scale
  • Depth in supervised and unsupervised learning, statistics, and experimentation, including A/B testing, power analysis, Bayesian methods, and causal inference
  • Command of the ML development lifecycle and MLOps: CI/CD, testing, observability, and reliable releases
  • Python
  • SQL or equivalent query languages

Nice to have

  • PhD preferred
  • Domain experience in bidding, pricing, elasticity modeling, capital allocation, search, personalization, ranking, or recommendation
  • Exposure to deep learning, LLMs, retrieval-based systems, and reinforcement learning
  • Java or Scala
  • Hands-on experience with ML and data engineering technologies such as Spark, Databricks, Kubernetes, and GPU compute
  • Discipline in data and feature engineering (quality, lineage, documentation) and in model design with clear objectives, constraints, and risk guardrails
  • Proficient communication, collaboration, and mentoring, with the ability to tailor complex concepts to technical and executive audiences

What the JD emphasized

  • Track record of delivering high-impact machine learning products from concept to production at scale
  • Depth in supervised and unsupervised learning, statistics, and experimentation, including A/B testing, power analysis, Bayesian methods, and causal inference
  • Command of the ML development lifecycle and MLOps: CI/CD, testing, observability, and reliable releases
  • Discipline in data and feature engineering (quality, lineage, documentation) and in model design with clear objectives, constraints, and risk guardrails

Other signals

  • lead applied science team
  • build models and optimization systems
  • deliver production ML at scale
  • own the roadmap
  • partner closely with Marketing, Product, Engineering, Finance, and Analytics leadership