Machine Learning Engineer

at  Hostelworld Group

ABOUT US

Hostelworld Group PLC is a ground-breaking social network powered OTA focused on the hostel market, with a clear mission to help travellers find people to hang out with.
Founded in 1999, we’re a well-known trusted brand with 240 teammates across 11 countries; hostel partners in 180 countries; and a strong commitment to building a better world in all that we do.
In particular, our focus in the last few years has been on improving the sustainability of the hostelling industry, through our membership of the Global Sustainable Tourism Council (GSTC); our active involvement in the Global Tourism Plastics Initiative (GTPI); our partnerships with Bureau Veritas to establish emissions benchmarks for the hostelling industry; and our recent partnership with the South Pole to offset all our greenhouse gas emissions in 2021 and 2022. You can read more about our story here: Hostelworld site.
Hostelworld is listed on the London Stock Exchange, Dublin Euronext and is an Investors in Diversity accredited employer.

EXPERIENCE & QUALIFICATIONS:

• Proficiency in machine learning concepts such as data exploration, feature engineering, model selection, training and testing.
• Proven experience in productization of machine learning solutions – libraries, packages, REST APIs, serving and monitoring.
• Familiarity with ranking, recommender systems, and natural language understanding.
• Familiarity with deep learning.

KNOWLEDGE & SKILLS:

• Proficiency programming in Python and SQL-like databases.
• You are proficient in at least one of the following machine learning tasks – classification, regression or clustering.
• You are familiar with at least two of the following libraries or packages – scikit-learn, pandas, Spark MLlib, TensorFlow or PyTorch.
• Ability to carefully design solutions that scale with increasing volumes of data.
• Ability to follow software engineering best practices, including requirements analysis, system design, modularity, version control, testing and documentation

WHAT YOU’LL DO ‍

• Translate business and customers problems into machine learning solutions.
• Defining hypotheses, measure and monitor the success.
• Propose, design, build, maintain and push machine learning solutions into production.
• Express and communicate the business and technical impact of the machine learning solutions to a variety of audiences.
• Responsible for the end-to-end process of designing and running experiments to serving production models at scale.
• Ensure that the solutions are maintainable, performant, scalable and debuggable.
• Automate and abstract away different repeatable routines that are present in most machine learning tasks.
• Provide support to software engineers and product managers building machine learning solutions.
• Bring the best software development and infrastructure practices to the data science squad.