Data Scientist

Posted 4 Days Ago
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Hiring Remotely in Office, Machaze, Manica, MOZ
Remote or Hybrid
Mid level
Other • Travel
The Role
Build recommendation systems that help travelers discover and choose travel options. Translate business questions into modeling problems, develop production-ready machine learning solutions, design and evaluate experiments, and collaborate with Data Science, Product, Engineering, and other teams. The role requires communicating technical findings and trade-offs clearly while delivering measurable improvements to user experience and business metrics.
Summary Generated by Built In

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 Zurich 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.

Benefits & Perks
  • 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

  • 5 weeks paid vacation + a day off for your birthday

  • Social events & Thursday happy hours

  • Free lunch 2 days per week

  • Pension plan contributions

  • Supplemental accident insurance

  • Health insurance subsidy of 250 CHF per month

  • Lunch Check Card of 180 CHF per month

  • Profawo membership which is a non-profit organization dedicated to promoting the compatibility of family and work

 
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

Skills Required

  • Strong foundations in statistics, machine learning, and programming
  • Track record of solving complex, real-world data science problems
  • Hands-on experience developing machine learning models in business or production environments
  • Ability to explain problems addressed, individual contributions, and resulting impact
  • Ownership, sound judgment, and analytical thinking
  • Ability to navigate technical trade-offs and work independently
  • Fluency with AI-assisted workflows and ability to critically evaluate their outputs and limitations
  • Curiosity, humility, collaboration, continuous learning, and knowledge sharing
  • Experience with PyTorch, Polars, CometML, Arize, Docker, Airflow, Trino, and Git/GitHub
  • Familiarity with Agile ways of working, including sprint planning and team ceremonies

KAYAK Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about KAYAK and has not been reviewed or approved by KAYAK.

  • Leave & Time Off Breadth — Time off is positioned as generous, with team‑wide downtime, paid volunteer time, and location‑specific vacation allotments in certain offices. Public descriptions also highlight generous PTO and paid sick days.
  • Healthcare Strength — Health coverage is described as comprehensive, including medical, dental, vision, and mental‑health support such as Headspace and access to counseling in some programs. Employer-verified listings also reference wellness resources and related benefits.
  • Fair & Transparent Compensation — Compensation philosophy emphasizes Equal Pay and a commitment to closing the global pay gap. Some job postings disclose salary ranges and note equity or bonus eligibility, reinforcing transparency for certain roles and locations.

KAYAK Insights

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The Company
HQ: Stamford, CT
1,002 Employees
Year Founded: 2004

What We Do

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, cruise, vacation package. We also support business travelers with KAYAK for Business, our free corporate travel solution and are transforming the in-travel experience with our app and new hotel and accommodation software. Want to join a talented team that’s eager to solve the world’s travel problems (and have a bit of fun)? As an employee of KAYAK, you’ll be part of a global network including OpenTable and a portfolio of travel metasearch brands like Swoodoo, checkfelix, momondo, Cheapflights, Mundi and Hotels Combined. So join us, and help others experience the world through dining and travel.

Why Work With Us

What's most unique about KAYAK is the way we empower our team to lead their best lives - in and out of the workplace. Our Work from Almost Anywhere policy is built for and by our team. It gives us the flexibility to choose where and how we work best.

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