Senior Manager, Machine Learning Science - Fraud & Risk

Expedia Expedia · Hospitality · Seattle, WA

Senior Manager, Machine Learning Science for Fraud & Risk at Expedia Group. Leads a team building ML models and AI systems to prevent fraud and abuse in the online travel marketplace. Focuses on supervised learning for tabular and text data, with familiarity in unsupervised, sequential, and graph-based methods. Drives innovation with modern techniques including sequence models, GNNs, and GenAI. Collaborates with product, engineering, and operations to ship real-time decisioning systems and improve performance.

What you'd actually do

  1. Lead, Mentor, and Develop: Mentor and grow a team of machine learning scientists, fostering a culture of innovation, collaboration and scientific rigor.
  2. Strategic Planning & Delivery: Define and manage the team's strategic roadmap, setting goals (OKRs) and aligning projects with broader business objectives in the online travel domain. Translate this roadmap into effective delivery of ML-drive features and products.
  3. Influence and Collaborate: Act as a key scientific leader, partnering with product, engineering, and business executives to align strategy and communicate complex technical concepts to a diverse audience.

Skills

Required

  • Python
  • SQL
  • PySpark
  • scikit-learn
  • Machine learning theory
  • Supervised learning
  • Unsupervised learning
  • Sequential models
  • Graph Neural Networks
  • People management
  • Strategic planning
  • Stakeholder influence
  • Communication

Nice to have

  • Fraud detection
  • E-commerce
  • GenAI
  • LLM technologies
  • Fine-tuning
  • Prompt engineering
  • Multi-agent architectures
  • Agentic AI design

What the JD emphasized

  • highly technical ML leader
  • supervised learning for tabular and text data
  • unsupervised, sequential, and graph‑based methods
  • shipping production ML systems
  • real-time decisioning systems
  • sequence models
  • graph-based approaches
  • GenAI techniques
  • PhD or MS in a quantitative field
  • 5+ years of industry experience applying machine learning
  • 2+ years of direct people management experience
  • Deep expertise in machine learning theory, and learning algorithms
  • Experience applying sequential models (e.g., RNNs, Transformers) and/or Graph Neural Networks (GNNs)
  • Hands-on experience building and deploying models using GenAI / LLM technologies

Other signals

  • leading a team of ML scientists
  • shipping production ML systems
  • real-time decisioning systems
  • GenAI techniques