Principal Machine Learning Scientist - Principal Machine Learning Scientist – Generative AI & C

Position: Principal Machine Learning Scientist - Principal Machine Learning Scientist – Generative AI & C[...]
Position Overview

Expedia Group’s Machine Learning and Data Science team is looking to hire researchers and data scientists who are passionate about using machine learning to improve customer experience. You will develop state‑of‑the‑art algorithms and generative AI solutions to power and enhance post‑booking recommendations, customer service, and trip‑management use cases. The role focuses on tackling substantial technical challenges, from inference problems in long‑tail traveler data to multi‑objective optimization in dynamic, operationally complex environments.

Responsibilities

Machine Learning Engineering

• Configure, maintain, and optimize storage and processing environments (cloud, on‑premises, cluster management, etc.).

• Build production‑grade data and machine learning pipelines that support batch and streaming applications.

• Advocate for software design best practices and construct robust data and machine learning pipelines.

Machine Learning / Data Science

• Perform applied research to consistently achieve desired solution performance and improve organizational capabilities.

• Demonstrate an in‑depth understanding of all aspects of learning theory.

• Support leaders in setting up frameworks for the machine learning development lifecycle and devise strategy.

• Advise business leaders to create and enable robust ML solutions with high impact at an expert level.

• Lead and mentor others in all machine learning activities.

Generative AI

• Deep expertise in LLM fine‑tuning and prompt engineering (e.g., OpenAI APIs, Hugging Face, Anthropic Claude, Google Gemini).

• Strong experience with AI orchestration tools (e.g., Lang Chain, Llama Index, vector databases for retrieval augmentation).

• Hands‑on knowledge of function calling and API‑based reasoning models.

• Proficiency in Python and AI development frameworks for building scalable AI applications.

• Understanding of multi‑agent architectures and best practices in agentic AI design.

• Experience with real‑world AI evaluation techniques, including golden sets, synthetic data generation, and interactive testing.

Statistics & Model Design

• Leverage a solid theoretical foundation and apply advanced statistical methods to a broad range of problems.

• Advanced experimental design (e.g., adaptive designs). Read relevant publications and implement described methods in business context.

• Design end‑to‑end models based on detailed business requirements, selecting algorithms and data sources.

• Ensure model output reflects deep business understanding, adhering to standard methodologies and the latest research.

• Demonstrate critical understanding of business processes and recommend solutions that meet unique business needs.

Visualization, Communication & Stakeholder Management

• Embed visualizations from tools, demonstrate proficiency in visualization tools, and apply data‑visualization principles consistently.

• Create complex charts, use color palettes, typography, and UX considerations to present clear visualizations.

• Be a persuasive storyteller, build trust with teams and partners, and influence across the organization.

• Mentor and train others on analytical problem‑solving, project management, and influencing for business impact.

• Manage stakeholders through frequent communication, expectation management, and timely delivery.

Minimum Qualifications

• Bachelor’s, Master’s, or Ph.D. in a technical field or equivalent experience.

• Expertise in at least two major ML programming languages (Python, R, Scala, etc.) and familiarity with others.

• Experience leading large data science technical programs, delivering successful outcomes with cross‑functional teams of 10+.

• Demonstrated contributions to the data science community through blog posts, talks, conferences, or similar.

• Experience defining data science best practices at a team/capability level.

• Expertise in configuring, maintaining, and optimizing storage and processing environments.

• In‑depth understanding of all aspects of machine learning theory and practice.

• Strong background in applying advanced statistical methods, including stochastic processes, Bayesian neural networks, Markov models, discriminant and…