Data Scientist

Das wird geboten

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: - Work from (almost) anywhere for up to 20 days per year - Focus on mental health and well-being: - Company-paid therapy sessions through SpringHealth - Company-paid subscription to HeadSpace - Company-wide week off a year - the whole team fully recharges (and returns without a pile-up of work!) - No meeting Fridays - Paid parental leave - Paid volunteer time - Focus on your career growth: - Development Dollars - Leadership development - Access to thousands of on-demand e-learnings - Travel Discounts - Employee Resource Groups - 6 weeks paid vacation + a day off for your birthday - Free lunch 2 days per week - Pension plan contributions - Public transportation subsidies - Bike leasing program - Monthly social events, Thursday happy hours, sports teams - An awesome office in Friedrichshain, Berlin Inclusion At KAYAK, we want everyone to have the space to grow, share ideas and do great work. That’s why we’re focused on hiring the best talent from all walks of life and experiences, supporting them well and making sure no one feels like they have to fit a mold to belong here. Need any adjustments for the interview, application or on the job? No problem - just give us a heads-up. We’ve got you. #LI-AS1

Zur Position

KAYAK, part of Booking Holdings (NASDAQ: BKNG), is the world s leading travel search engine. With billions of queries across our platforms, we help people find their perfect flight, stay, rental car and vacation package. We re also transforming business travel with a new corporate travel solution, KAYAK for Business.

As an employee of KAYAK, you will be part of a travel company that operates a portfolio of global metasearch brands including momondo, Cheapflights and HotelsCombined, among others. From start-up to industry leader, innovation is at our core and every employee has an opportunity to make their mark. Our focus is on building the best travel search engine to make it easier for everyone to experience the world.

KAYAK is looking for a mid-level Data Scientist to build recommendation systems that improve how travelers discover and choose travel options. You’ll translate product and business questions into measurable modeling problems, develop and test solutions with data and experimentation, and partner with researchers, engineers, and cross-functional teams to bring promising work into production. Your work will improve the traveler experience and key business metrics across KAYAK.

If you’re excited to solve challenging recommendation problems and make a measurable difference for travelers, we’d love to hear from you. This role will be required to work from our Berlin office 3 days per week. In this role, you will: - Translate product and business challenges into well-defined modeling problems, hypotheses, and measurable objectives. - Ship production-ready, scalable Machine Learning solutions with real-world impact on user experience and key business metrics. - Design and evaluate experiments, building rapid prototypes, assessing results rigorously, and iterating on promising ideas. - Collaborate across Data Science, Product, and Engineering, communicating findings, recommendations, and trade-offs clearly and concisely to both technical and non-technical audiences.

Please apply if you have: - Strong technical foundations in statistics, machine learning, and programming, backed by a track record of solving complex, real-world data science problems. Demonstrated capability matters more than academic credentials. - Hands-on experience developing ML models in business or production environments, with the ability to clearly explain the problems addressed, your specific contributions, and the resulting impact. - Ownership, sound judgment, and analytical thinking, with the ability to navigate technical trade-offs, work independently, and recognize when to seek guidance. - Fluency with AI-assisted workflows, leveraging modern tools to improve productivity and quality while critically evaluating their outputs and limitations. - Curiosity, humility, and a collaborative mindset, with a commitment to continuous learning, sharing knowledge, and improving together. Bonus qualifications - Hands-on experience with technologies relevant to KAYAK’s stack, such as PyTorch, Polars, CometML, Arize, Docker, Airflow, Trino and Git/Github. - Familiarity with Agile ways of working, such as sprint planning and team ceremonies.