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Machine Learning Scientist II - Job Opportunity at Expedia Group

London, United Kingdom
Full-time
Mid-level
Posted: January 25, 2025
Hybrid
GBP 75,000 - 95,000 annually based on London market rates for ML Scientist II roles in travel tech

Benefits

Global hybrid work arrangement with modern office access
Comprehensive travel perks package
Generous time-off policy
Parental leave program
Career development resources
Full benefits package aligned with tech industry standards

Key Responsibilities

Lead machine learning problem formulation and solution design for lodging search optimization
Develop and deploy production-grade ranking models using state-of-the-art techniques
Conduct advanced data analysis and feature engineering for recommendation systems
Collaborate with cross-functional teams to integrate ML solutions
Present technical findings and results to senior stakeholders
Drive A/B testing and performance measurement of deployed models

Requirements

Education

Master's degree or Ph.D. in Computer Science, Statistics, Math, Engineering, or related technical field; or equivalent related professional experience

Experience

Not explicitly specified, but role level suggests 3-5 years

Required Skills

Modern machine learning techniques Statistics and hypothesis testing Recommender systems experience (preferred) Python programming Deep learning frameworks (TensorFlow or PyTorch) SQL Distributed computing with Spark (preferred) Data wrangling Feature engineering Model building Data visualization
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Sauge AI Market Intelligence

Industry Trends

Travel industry's increasing focus on personalization and AI-driven recommendations represents a strategic shift in how travel products are marketed and sold Growing emphasis on real-time optimization and dynamic pricing in the travel sector is driving demand for advanced ML capabilities Integration of multiple data sources and cross-platform optimization is becoming crucial for travel tech companies

Role Significance

Typically part of a specialized ML team of 5-8 engineers, working within a larger technology organization
Mid to senior individual contributor role with significant technical impact on core business metrics

Key Projects

Search ranking optimization systems Personalized recommendation engines Large-scale A/B testing frameworks Production ML pipeline development

Success Factors

Strong foundation in both theoretical ML concepts and practical implementation Ability to translate business requirements into technical solutions Experience with large-scale production systems Excellent communication skills for stakeholder management

Market Demand

High demand position with significant growth potential due to travel industry's digital transformation and increasing focus on AI/ML technologies

Important Skills

Critical Skills

Production ML deployment experience - essential for immediate impact Python and deep learning frameworks - core technical requirement Data analysis and feature engineering - fundamental to role success A/B testing and metrics analysis - crucial for measuring business impact

Beneficial Skills

Cloud computing platforms Recommendation system design patterns Travel industry domain knowledge Distributed systems experience

Unique Aspects

Direct impact on millions of travel decisions through ML model deployment
Access to unique travel industry datasets and user behavior patterns
Opportunity to work on multi-brand optimization challenges
Complex three-sided marketplace optimization problems

Career Growth

2-3 years in role before advancement, depending on impact and organizational growth

Potential Next Roles

Senior Machine Learning Scientist Technical Lead - ML ML Architect Principal Scientist

Company Overview

Expedia Group

Expedia Group is a global travel technology leader operating multiple major travel brands and platforms

One of the world's largest online travel agencies with significant market share and technical innovation capacity
Strong presence in London tech hub with significant ML/AI operations
Technology-driven culture with emphasis on innovation, collaboration, and data-driven decision making
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