ML Engineer

Summary

Join Cloudbeds, a leading hospitality platform, as a Machine Learning Engineer! Based in South Kensington, London (hybrid), you'll build and implement features to optimize revenue for our lodging customers. This involves developing machine learning models, collaborating with cross-functional teams, and analyzing data to identify trends. You'll need a Bachelor's degree in a quantitative field, 3+ years of data engineering experience (preferably in hospitality), and expertise in machine learning and cloud computing. Cloudbeds offers a remote-first culture, flexible working schedules, open Paid Time Away, and opportunities for professional development.

Requirements

• Bachelor's degree or higher in a quantitative field such as Computer Science, Statistics, Mathematics, or Data Science
• 3+ years of experience in a data engineering role, preferably in the hospitality industry
• Expertise deploying machine learning models on the Cloud at scale, e.g. using MLFlow
• Expert level knowledge of SQL and experience working with large datasets
• Expertise with AWS or other cloud computing platform
• Experience implementing machine learning models using Python, including familiarity with popular data science frameworks such as Pandas, Scikit-Learn
• Strong problem-solving skills and ability to think creatively about complex business problems
• Strong application development skills
• Excellent communication skills and ability to collaborate effectively with cross-functional teams

Responsibilities

• Build and implement end-to-end features and functionality that allow our lodging customers to optimize their revenue, through making informed pricing decisions. Some of these features will make us of simple heuristic data while others may involve algorithmic or machine learning components
• Where needed, develop and implement machine learning models to optimize revenue generating opportunities for our customers
• Collaborate with cross-functional teams to identify areas for product improvement
• If needed, figure out ways to structure data to make it easy to analyze and apply learning algorithms
• Build and maintain data pipelines to extract and transform data from various sources
• Analyze data sets to identify trends and patterns that inform product development and marketing strategies
• Design and conduct experiments to test the effectiveness of new features and improvements
• Communicate findings and insights to stakeholders across the organization, including product, engineering, and customer success teams

Benefits

• Flexible working schedules
• Open Paid Time Away policy
• Opportunity to travel and work remotely
• Access to professional development, including manager training, upskilling and knowledge transfer