At a Glance
- Tasks: Develop and optimise machine learning models to enhance travel experiences for millions.
- Company: Join a leading global travel tech company with a vibrant, inclusive culture.
- Benefits: Enjoy travel perks, generous time-off, flexible work, and career development resources.
- Why this job: Make a real impact in the travel industry using cutting-edge machine learning technology.
- Qualifications: Master's or Ph.D. in a technical field with 2+ years of ML experience.
- Other info: Collaborative environment with opportunities for innovation and growth.
The predicted salary is between 28800 - 48000 ÂŁ per year.
Expedia Group brands power global travel for everyone, everywhere. We design cuttingâedge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success.
Why Join Us?
To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and when one of us wins, we all win. We provide a full benefits package, including exciting travel perks, generous timeâoff, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. Weâre building a more open world. Join us.
Introduction to the team
The Machine Learning Scientist II role sits on the Content Relevance Ranking AI team in the Expedia Technology division of Expedia Group. This team develops and optimizes ranking models with stateâofâtheâart machine learning techniques to power the selection and ranking of property images and reviews for the multiple brands in our portfolio.
In this role, your expertise and passion for innovation, developing cuttingâedge technology and implementing industryâleading solutions, will improve the experience of millions of travelers and travel partners each year. This is an applied research role: your models will be deployed to our production systems, and your results will be measured objectively via A/B testing, directly impacting our business results. We collaborate closely with the analytics, product, and engineering teams.
In this role, you will:
- Work with product management to understand business problems, identify challenges and machine learning opportunities, and scope solutions.
- Conduct exploratory data analysis, formulate machine learning problems, and build effective models.
- Partner with data and software engineering teams to deliver your solutions into production.
- Develop a deep understanding of our data and ML infrastructure.
- Document the technical details of your work.
- Present your ideas and results to product management, stakeholders, and leadership teams in a clear and effective manner.
- Collaborate and brainstorm with other team members and across the company.
- Stay current with advances in ML and GenAI to drive innovation within the team.
Minimum Qualifications:
- Master's degree or Ph.D. in Computer Science, Statistics, Math, Engineering, or a related technical field; or equivalent related professional experience.
- You have 2+ years handsâon experience with ML in production, building datasets, selecting and engineering features, building and optimizing algorithms.
- You have expertise with Python and related machine learning tools, deep learning frameworks such as TensorFlow or PyTorch, and SQLâlike query languages for data extraction, transformation, and loading.
- A strong foundation in Machine Learning fundamentals, statistics, and experimentation.
- You have realâworld experience working with large data sets in a distributed computing environment such as Spark.
- You have good programming practices, ability to write readable, fast code.
- You have intellectual curiosity and desire to learn new things, techniques and technologies.
Preferred Qualifications:
- Experience with ranking systems and recent Large Language Models (LLMs), including fineâtuning, efficient deployment, and architectures.
- Comfortable working with ML platforms like Databricks and Cloud platforms such as AWS, and Docker.
- Handsâon experience with workflow orchestration tools (e.g., Airflow, Flyte).
Accommodation requests
If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request.
We are proud to be named as a Best Place to Work on Glassdoor in 2024 and be recognized for awardâwinning culture by organizations like Forbes, TIME, Disability:IN, and others.
Expedia Group's family of brands includes: Brand Expedia, Hotels.com, Expedia Partner Solutions, Vrbo, trivago, Orbitz, Travelocity, Hotwire, Wotif, ebookers, CheapTickets, Expedia GroupTM Media Solutions, Expedia Local Expert, CarRentals.comTM, and Expedia CruisesTM.
Machine Learning Scientist II in London employer: PowerToFly
Contact Detail:
PowerToFly Recruiting Team
StudySmarter Expert Advice đ€«
We think this is how you could land Machine Learning Scientist II in London
âšTip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Expedia Group. A friendly chat can open doors and give you insights that a job description just can't.
âšTip Number 2
Show off your skills! Prepare a portfolio or a project that highlights your machine learning expertise. When you get the chance to chat with recruiters or hiring managers, having something tangible to discuss can really set you apart.
âšTip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your coding skills and ML concepts. Use platforms like LeetCode or HackerRank to simulate the interview experience and boost your confidence.
âšTip Number 4
Apply through our website! Itâs the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the Expedia family. Donât miss out!
We think you need these skills to ace Machine Learning Scientist II in London
Some tips for your application đ«Ą
Tailor Your Application: Make sure to customise your CV and cover letter for the Machine Learning Scientist II role. Highlight your relevant experience with ML, Python, and any specific projects that align with what we do at Expedia Group.
Showcase Your Passion: Let us see your enthusiasm for machine learning and travel! Share any personal projects or research that demonstrate your innovative spirit and how you stay current with industry trends.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to explain your skills and experiences, making it easy for us to see how you can contribute to our team.
Apply Through Our Website: Donât forget to submit your application through our official website! This ensures that your application goes directly to our hiring team and helps us keep track of all candidates.
How to prepare for a job interview at PowerToFly
âšKnow Your Stuff
Make sure you brush up on your machine learning fundamentals, especially around ranking systems and large language models. Be ready to discuss your hands-on experience with Python, TensorFlow, or PyTorch, and how you've applied these in real-world scenarios.
âšShow Your Problem-Solving Skills
Prepare to talk about specific business problems you've tackled using machine learning. Think of examples where you identified challenges, scoped solutions, and collaborated with product management or engineering teams to deliver results.
âšCommunicate Clearly
Practice presenting your ideas and results in a clear and effective manner. You might be asked to explain complex concepts to non-technical stakeholders, so being able to simplify your findings is key. Use visuals if you can!
âšStay Current and Curious
Demonstrate your intellectual curiosity by discussing recent advancements in ML and GenAI. Show that you're not just knowledgeable but also passionate about learning new techniques and technologies that could benefit the team.