Senior Machine Learning Scientist

About the role

The Senior Machine Learning Scientist will lead the development of GenAI- and LLM-powered solutions that enhance post-booking customer experiences, including recommendations, customer service, and trip management. This role involves owning the entire ML lifecycle, from problem framing to model deployment and continuous evaluation. You will also partner with product, engineering, and operations teams, mentor junior scientists, and contribute to best practices for AI agent development and evaluation.

Responsibilities

• Own the end-to-end ML lifecycle for medium-to-large projects, including problem framing, data preparation, model/agent design, orchestration, deployment, and continuous evaluation.
• Design and implement ML solutions, including batch and streaming ML systems, 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.
• 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 for customer support, content generation, and workflow automation.
• 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.

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.
• Hands-on experience using GenAI / LLM APIs (e.g., OpenAI, Hugging Face, Anthropic, Gemini) in prototypes or production.
• 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.
• 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.
• 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).

Qualifications

• Experience & Education: 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.
• Functional & Technical Skills: Applied ML & Statistics, 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, Engineering & Tooling, Strong programming skills in Python and its data/ML ecosystem (e.g., pandas, scikit-learn, PyTorch/TensorFlow, PySpark), 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, Generative AI & Advanced Methods, 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.

Skills

• Applied ML & Statistics
• 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
• Engineering & Tooling
• Strong programming skills in Python and its data/ML ecosystem (e.g., pandas, scikit-learn, PyTorch/TensorFlow, PySpark)
• 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
• Generative AI & Advanced Methods
• 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

Experience

• Experience & Education: 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.
• Functional & Technical Skills: Applied ML & Statistics, 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, Engineering & Tooling, Strong programming skills in Python and its data/ML ecosystem (e.g., pandas, scikit-learn, PyTorch/TensorFlow, PySpark), 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, Generative AI & Advanced Methods, 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

Desired Experience

• 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).

Benefits & Perks

• Medical, dental, and vision coverage
• Paid time off
• An Employee Assistance Program
• Wellness and travel reimbursement
• Travel discounts
• IATAN membership

About Expedia Group

Expedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.

Important Notice

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