Data Scientist - Decision Tooling

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 100 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.)

Now, we’re on the lookout for a Data Scientist to join our Decision Tooling team — helping Skyscanner make smarter, faster, and more confident decisions through machine learning, experimentation and statistical modelling.

Hybrid

Skyscanner runs on data — and the Decision Tooling team makes that data work harder. We design, build and scale the internal ML and statistical tools that help our product and commercial teams forecast demand, measure experiments, detect anomalies, and optimise performance.

We’re a cross-functional group of data scientists and engineers working at the intersection of ML, statistics and software development. Whether it’s causal inference, bot detection or business forecasting, our job is to turn complex modelling into scalable, reliable systems that unlock better decisions across the company.

You’ll have the opportunity to work across the full ML lifecycle — partnering with teams across Skyscanner to build tools, shape best practices, and help others learn and act with confidence.

• Designing, building, and deploying end-to-end ML systems for forecasting, anomaly and bot detection, causal inference, and revenue or lifetime value modelling.

• Partnering with product and engineering teams to build and maintain shared ML libraries and data pipelines that make modelling consistent and scalable company-wide.

• Mentoring other data scientists and engineers in statistical experimentation, causal inference, and robust ML evaluation, helping establish best practices across Skyscanner.

• Working cross-functionally to identify new opportunities where ML and statistical systems can unlock better decisions at scale.

• You’ve delivered applied machine learning models into production in commercial, at-scale environments

• You bring solid foundations in statistics and experimental design

• You’re confident coding in Python and PySpark, and familiar with key ML and statistical libraries

• You’ve worked with modern ML infrastructure — including data pipelines, orchestration, CI/CD, deployment and monitoring

• You’re comfortable collaborating with engineers to build robust, testable code that scales

• You communicate clearly — able to explain ML and statistical concepts to both technical and non-technical audiences

• You take a pragmatic, product-minded approach — focused on adoption, reliability and real-world impact, not just model metrics

• You’re curious, collaborative and equally happy working independently or as part of a cross-functional team

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? Apply now and help us shape the future of travel.

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.