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KAYAK, part of Booking Holdings, is the world's leading travel search engine. The company operates a portfolio of global metasearch brands including momondo, Cheapflights, and HotelsCombined. You will join a team focused on building recommendation systems that improve how travelers discover and choose travel options.
In this role, you will 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 work into production. Your responsibilities include:
- Translating product and business challenges into well-defined modeling problems, hypotheses, and measurable objectives
- Shipping production-ready, scalable machine learning solutions with real-world impact on user experience and key business metrics
- Designing and evaluating experiments, building rapid prototypes, assessing results rigorously, and iterating on promising ideas
- Collaborating across Data Science, Product, and Engineering teams, communicating findings and recommendations clearly to both technical and non-technical audiences
The role is based in the Berlin office and requires 3 days per week on-site. You will have flexibility to work remotely up to 20 days per year.
Benefits include 6 weeks paid vacation plus a birthday day off, paid parental leave, company-paid therapy and wellness subscriptions, no-meeting Fridays, development dollars for career growth, leadership development programs, travel discounts, public transportation subsidies, bike leasing, free lunch twice weekly, and regular social events.
REQUIREMENTS:
- Strong technical foundations in statistics, machine learning, and programming, with a demonstrated track record of solving complex, real-world data science problems
- Hands-on experience developing ML models in business or production environments, with ability to clearly explain problems addressed, your contributions, and resulting impact
- Ownership, sound judgment, and analytical thinking; ability to navigate technical trade-offs, work independently, and recognize when to seek guidance
- Fluency with AI-assisted workflows, leveraging modern tools while critically evaluating outputs and limitations
- Curiosity, humility, and collaborative mindset with commitment to continuous learning and knowledge sharing
BONUS QUALIFICATIONS:
- Hands-on experience with PyTorch, Polars, CometML, Arize, Docker, Airflow, Trino, and Git/Github
- Familiarity with Agile ways of working such as sprint planning and team ceremonies
