About Skyscanner

Everyone loves travelling, but planning is not without its challenges. That's why we've spent 20 years building tools that turn travel-planning chaos into a breeze. Today, around 160 million travellers count on us every month to skip the whole "47 browser tabs open" phase and find flights, cars, and hotels quickly and easily.

Joining Skyscanner means becoming part of a global brand that's striving to become the planet's go-to travel hack accessible for all.

Our vision? To be the world's number one travel ally. (Ambitious? Yes, but, hey, that's what got us here.)

About the role
(Hybrid)

• Powering better decisions: You'll join our Decision Tooling team - the folks building the statistical and machine learning systems that help Skyscanner teams make smarter calls, faster.
• Making the complex feel clear: From forecasting demand to detecting anomalies and evaluating experiments, you'll help build reliable, interpretable systems people can actually trust - and act on.
• Raising the bar, together: You'll own well-defined components of larger modelling systems, while helping level up modelling standards across the team (think: quality, robustness, and "this will still work next quarter").

What you'll be doing

• Building decision-grade models: You will design and deliver ML and statistical systems that directly power real business decisions across Skyscanner.
• Turning ambiguity into frameworks: You will translate messy, open-ended problems into clear modelling approaches and crisp assumptions.
• Forecasting & anomaly detection: You will build and validate forecasting and anomaly detection solutions that spot issues early.
• Experimentation tooling: You will develop experimentation solutions that help teams evaluate change with confidence and clarity.
• Defining robust evaluation: You will set up backtesting, monitoring, and evaluation strategies that prove models are working - and keep them honest over time.
• Embedding into production workflows: You will partner closely with Engineering and Product to integrate models into production systems and day-to-day decision-making.
• Owning meaningful components: You will take ownership of well-scoped parts of larger systems and contribute to improving modelling standards across the team.

 
About you

• Production-tested: You have 2+ years' experience as a Data Scientist working in a production environment.
• Applied ML experience: You've worked on real-world machine learning or applied data science problems (bonus points if you have worked on experimentation and casual inference).
• Strong fundamentals: You bring solid foundations in statistics, machine learning, or quantitative analysis, and, you know when to use which tool.
• Hands-on with Python & SQL: You're comfortable building in Python and querying data with SQL.
• Team-friendly engineer mindset: You're familiar with collaborative development practices like version control and code review.
• Structured problem-solver: You can take an ambiguous problem, break it down methodically, and move it forward without needing a perfect brief.
• Thoughtful evaluator: You care about robust evaluation, including fairness and reliability considerations.

What it's like here

We are the real deal - no corporate gloss, no empty promises. Just a team of genuinely curious, caring humans, building things that help travellers explore the world a little easier.

Skyscanner is made up of brilliant humans from every corner of the world. We believe travel makes the world better - and that the same is true of our diverse teams. We're proud to be an equal opportunities employer and are committed to building an inclusive workplace where everyone can thrive and products that are accessible to all.

Sound like your kind of adventure?

We're committed to ensuring our application and recruitment processes are inclusive and accessible to everyone. If you require any reasonable adjustments or accommodations for interviews, and/or wish to apply under the Disability Confident scheme, please let your recruiter know. If you'd like more information on any of our policies, such as hybrid working or Parental Leave policies (typically we pay a minimum of 24 weeks birth parent/maternity leave globally), our recruitment team can provide more information on these.