Now, we're on the lookout for a Senior Data Scientist to help build the statistical backbone of how we test, learn, and make decisions across Skyscanner - enabling teams to experiment with confidence and deliver impact at scale!
About the role

Hybrid

This is a high-impact individual contributor role in our internal experimentation platform team, Nightingale. You'll work at the intersection of data science, product, and engineering to design, implement and scale the statistical foundations that power product experimentation at Skyscanner.

From Bayesian inference to causal modelling, you'll help shape the tools and methods that ensure product decisions are grounded in robust, reliable evidence - improving the travel experience for millions of users worldwide.
What you'll be doing

• Partnering with engineers and product managers to evolve Skyscanner's internal experimentation platform
• Designing and implementing advanced statistical methods - particularly Bayesian and causal inference techniques - to meet the needs of complex experiments
• Owning and scaling the inference libraries that power experiment analysis across the business
• Researching, building, and maintaining new product features that make experimentation faster, easier, and more rigorous
• Advocating for best practices in experimentation design and causal reasoning
• Acting as a domain expert for statistical methods and experimentation - mentoring others and influencing platform direction
• Collaborating across disciplines to continuously improve experimentation tooling and reliability

• Statistically strong: You have deep knowledge of Bayesian statistics, causal inference, and experimental design
• Technically sharp: You write high-quality, production-ready Python and are fluent in SQL
• Platform-aware: You've worked closely with engineers to build and ship data products, statistical libraries, or backend services
• Experimentation fluent: You've contributed to or built experimentation infrastructure and understand the nuances of measurement at scale
• Comfortable with deployment workflows: Familiar with Git, CI/CD, Docker, and statistical programming best practices
• Clear communicator: You explain statistical concepts to technical and non-technical audiences with clarity and impact
• Product-minded: You think like a builder - creating tools that are intuitive, scalable, and designed for use across the business
• Collaborative by nature: You work autonomously while building strong partnerships with engineers, PMs, and fellow data scientists
• Sustainability and accessibility focused: You think in systems and solutions, always aiming for simplicity, inclusivity and long- term value.