Senior Manager, Machine Learning Science

Expedia Expedia · Hospitality · Bangalore, India

Senior Manager, Machine Learning Science at Expedia, leading a team of 5-10 data scientists to develop and deploy ML algorithms for customer experience enhancement in online travel, focusing on searches, recommendations, and advertising. The role involves strategic roadmap definition, technical leadership, and collaboration with product and engineering partners to drive business growth through data-rich solutions.

What you'd actually do

  1. Mentor and grow a team of 5-10 machine learning scientists, fostering a culture of innovation, collaboration and scientific rigor.
  2. Define and manage the team's strategic roadmap, setting goals (OKRs) and aligning projects with broader business objectives in the online travel domain, and translate this roadmap into effective delivery of ML-drive features and products.
  3. 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

  • PhD or MS in a quantitative field (e.g., Computer Science, Economics, Statistics, Physics)
  • 5+ years of industry experience applying machine learning to solve real-world problems
  • 2+ years of direct people management experience
  • Deep expertise in machine learning theory
  • Statistical methods
  • Scientific principles of measurement
  • End-to-end ML lifecycle understanding
  • Python
  • PySpark
  • scikit-learn
  • SQL

Nice to have

  • Advanced domain knowledge in online advertising and/or e-commerce
  • Experience building and deploying models using GenAI/LLM technologies

What the JD emphasized

  • 5+ years of industry experience applying machine learning to solve real-world problems
  • 2+ years of direct people management experience with a proven track record of hiring, mentoring, and developing a high-performing team of scientists or engineers
  • Deep expertise in machine learning theory, statistical methods, and the scientific principles of measurement necessary to guide the team's technical direction and ensure scientific rigor
  • A comprehensive understanding of the end-to-end ML lifecycle, from ideation and research to deployment and monitoring
  • Hands-on fluency in Python and its data science ecosystem (e.g., PySpark, scikit-learn), and SQL
  • Technical depth to unblock your team and contribute to architectural decisions

Other signals

  • customer experience
  • recommendations
  • advertising marketplace
  • predictive modeling
  • marketplace optimization
  • causal measurement