Senior Machine Learning Scientist

Expedia Group•San Jose, CA

1d•Hybrid

About The Position

The Senior Machine Learning Scientist is responsible for building and evaluating GenAI- and LLM-powered solutions and AI agents that improve post-booking customer experience, including recommendations, customer service, and trip management. This role involves owning end-to-end ML and GenAI projects, from problem framing and data preparation through model/agent design, orchestration, deployment, and continuous evaluation. The scientist will apply deep expertise in applied ML, Generative AI, and rigorous experimentation to design robust evaluation frameworks (A/B tests, offline metrics, qualitative assessments) to ensure agents are safe, effective, and aligned with business goals. Close partnership with product, engineering, and operations is required, along with mentoring junior scientists and helping define best practices for AI agent development and evaluation. This position is for individuals passionate about using machine learning to improve customer experience at scale within the fast-paced, competitive, customer-focused, and data-rich world of online travel. The Machine Learning and Data Science team is growing and seeks a Senior Machine Learning Scientist to tackle complex customer experience problems in the travel domain. The role involves developing state-of-the-art machine learning and AI solutions to power and enhance customer experience across post-booking recommendations, customer service, and trip management use cases. Challenges include inference problems on long-tail traveler data and multi-objective optimization in a dynamic, operationally complex customer service environment. The role requires a passion for machine learning, causal inference, and Generative AI to unlock business growth by exploiting rich datasets and building effective solutions for travelers and partners. This is an opportunity to build core algorithms that bring context and intelligence to every step of the traveler journey and redefine service excellence in travel. The ideal candidate is a hands-on senior scientist who can independently drive impactful projects, mentor others, and collaborate closely with partners to make travel more seamless for millions of customers and partners worldwide.

Requirements

PhD in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Economics, Operations Research) and ~3+ years of industry experience; or Master’s degree in a quantitative field with ~5+ years of relevant industry experience.
Proven track record of building and deploying ML models that meaningfully impact business metrics in a production environment.
Strong knowledge of machine learning theory and practice (e.g., supervised learning, representation learning, ranking/recommendation, deep learning).
Solid grounding in statistics, experimental design (A/B testing), and basic causal inference; comfortable designing and analyzing online experiments.
Able to design end-to-end ML solutions: frame the problem, choose data sources, select algorithms, define evaluation strategies, and iterate based on results.
Strong programming skills in Python and its data/ML ecosystem (e.g., pandas, scikit-learn, PyTorch/TensorFlow, PySpark), plus proficiency in SQL.
Experience working with cloud-based data/compute platforms and modern data/ML tooling (e.g., Spark, Airflow, feature stores, model serving frameworks).
Follow software engineering best practices (version control, code reviews, testing, documentation) and contribute to shared libraries and tooling.
First-principles problem solver: able to decompose ambiguous problems, identify key assumptions, and design pragmatic, iterative solutions.
Excellent written and verbal communication skills; able to tell a compelling story with data and models and influence decisions.
Collaborative and customer-obsessed, with the ability to balance scientific rigor and engineering pragmatism in a product environment.

Nice To Haves

Hands-on experience using GenAI / LLM APIs (e.g., OpenAI, Hugging Face, Anthropic, Gemini) in prototypes or production is highly desired.
Familiarity with concepts like prompt engineering, retrieval-augmented generation, function/tool calling, and evaluation of LLM-based systems.
Experience with reinforcement learning, bandits, or other advanced ML techniques is a plus.
Domain experience in customer service, recommendations, personalization, or e-commerce applications.
Experience building ML systems for operational decision-making (e.g., contact routing, triage, capacity/effort prediction, workflow optimization).
Experience mentoring other scientists or engineers and contributing to technical culture (e.g., brown bags, tech talks, documentation, best practices).

Responsibilities

Own the end-to-end ML lifecycle for medium-to-large projects: from problem framing and ideation through research, prototyping, deployment, and post-launch monitoring.
Design robust, scalable ML systems (batch and/or streaming) in partnership with engineering, including data pipelines, feature computation, and model serving.
Translate ambiguous business problems into well-defined ML problems with clear success metrics and validation strategies.
Develop, evaluate, and iterate on supervised, unsupervised, and deep learning models for prediction, recommendation, and optimization.
Apply causal inference and experimental design (A/B testing) to accurately measure impact and guide decision-making.
Read and apply relevant academic and industry research to improve model architectures, training strategies, and evaluation methods.
Contribute to defining best practices for experimentation and modeling within the team; help raise the technical bar for ML development.
Build and iterate on models and applications leveraging GenAI / LLM technologies (e.g., OpenAI, Hugging Face, Anthropic, Gemini) for customer support, content generation, and workflow automation.
Use prompting, retrieval-augmented generation, and tool/function-calling patterns to integrate LLMs into production systems.
Explore and prototype advanced ML techniques (e.g., reinforcement learning, sequence modeling, transformers) where they can provide clear business value.
Design end-to-end modeling approaches, including data selection, feature engineering, algorithm choice, training procedures, and evaluation.
Apply statistical rigor in analyzing experiments and observational data; quantify uncertainty, trade-offs, and model risk.
Define and monitor offline and online metrics that faithfully reflect business goals (e.g., customer satisfaction, cost-to-serve, operational efficiency).
Partner closely with product managers, engineers, analysts, and operations to understand requirements, define roadmaps, and align on priorities.
Communicate complex technical concepts in a clear, concise way to technical and non-technical stakeholders.
Build intuitive dashboards and visualizations to explain model behavior, experiment results, and business impact.
Lead cross-functional projects involving multiple partners (e.g., product, engineering, operations), driving them from conception to measurable impact.
Manage project scope, timelines, and communication, proactively surfacing risks and trade-offs.
Mentor junior scientists and engineers on modeling approaches, experimentation, and analytical problem solving.