As you contemplate your future, you might be asking yourself, what's the next step? Start your journey with us!
We're seeking an experienced Data Scientist to join our Data Science Team in Barcelona. If you're ready to soar, we're ready to take you there. You will have the opportunity to either crunch massive datasets using the latest Big Data technologies or solve a quick problem with an already classic algorithm. We always promote using the best tool for each task. You will be a key player in the organization by designing and implementing advanced analytics and using machine learning to build AI-powered solutions for various areas in the business such as online fraud detection, recommendation engines, user experience personalization, and optimizing marketing campaigns. Join us and help us reach our ambitious goal of becoming the global leader in online travel!
Our dynamic team of young, talented professionals from over 60 different nationalities is driven by one clear mission: making travel easier, more accessible, and creating better value for our 18 million customers worldwide. Inside our Data Science team, we research and build cutting-edge solutions to offer more tailored travel plans than any other site. We have dozens of machine learning models in production doing hundreds of thousands of predictions that optimize our processes, improve the content for our customers, and help us adapt to changes in the market.
What you will do: As a Data Scientist in the Data Science team, you will be in charge of:
Conducting predictive analytics to better understand how our customers interact with our products and help us anticipate their needs. Collaborating with Product and Business teams to embed Machine Learning into our apps, services, and products, creating predictive models and generating insights from our data. Coordinating, challenging, and sharing the best analytical practices and methods within the organization. Staying up to date with the latest developments in Machine Learning technologies and Data Science trends. Effectively communicating results and insights with other stakeholders throughout the organization. What you need to succeed: Degree in a quantitative field (Mathematics, Statistics, Computer Science, Engineering or other quantitative fields). 2+ years of related experience as a Data Scientist, Machine Learning engineer, or similar. Previous experience in problem-solving, strong analytical and technical skills. Solid understanding of appropriate statistical, data mining, and machine learning methods for various analysis situations. Strong experience coding in Python. Other programming languages like Scala, R, or Java are also valued. Experience using frameworks like Spark, Tensorflow, and libraries like scikit-learn, pandas, etc. Solid knowledge and use of SQL is important. Experience in Big Data technologies such as distributed computing, NoSQL/graph databases, or Kubernetes, Airflow, or Kafka would be a plus. Commitment to following good practices and conventions, from code versioning to QA processes and monitoring. Written and oral communication skills (in English). What's in it for you? A rewarding Compensation package including competitive salary and benefits, flexible benefits, performance-based bonuses, and relocation support. Continuous learning opportunities with free Coursera access, tech training, leadership development, and a great onboarding program. Personalized career paths and internal mobility opportunities to empower your career. Flexibility with a hybrid home-office model focused on outcomes. Fun after-work events and a dynamic, innovative environment that supports high performance, learning, and growth. If you are ready for a career opportunity with unmatched benefits, continuous learning, and a supportive work-life balance, look no further! Take your career to new destinations by applying now and help our diverse, inclusive, and passionate team shape the future of travel.
Apply now! We are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status.
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